Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success.Ready to join us?Heres what the opportunity supported through our TGS Talent Acquisition Team requires:The Mid-Level AI/ML Engineer Generative AI is a hands-on technical contributor responsible for building, deploying, and maintaining production-grade AI/ML and Generative AI solutions across client and internal initiatives. This role requires solid software engineering fundamentals, practical experience with Python, cloud-native AI services, and hands-on delivery of AI-enabled applications using large language models, retrieval-augmented generation, embeddings, vector search, prompt engineering, and MLOps practices.This role sits at the intersection of software engineering, AI integration, data engineering, cloud delivery, and responsible AI. The ideal candidate can translate well-defined business requirements into secure, scalable, and maintainable AI solutions; integrate AI capabilities into enterprise applications; evaluate model and system behavior; and contribute to operational maturity after deployment.Mid-Level AI/ML Engineers are expected to work independently on well-defined assignments, collaborate on architectural and technical decisions, seek guidance for highly complex or ambiguous work, and contribute to delivery, reliability, observability, and responsible AI practices across teams.Top 3 Hiring Priorities1. Hands-on Generative AI and AI/ML engineering experience, including practical implementation of LLM-powered applications, RAG pipelines, embeddings, vector search, prompt engineering, and AI service integration.2. Strong Python and software engineering fundamentals, demonstrated through clean, maintainable, well-tested code; API development; modular architecture; data handling; and sound technical trade-offs for performance, scalability, reliability, and security.3. Ability to productionize AI solutions in cloud environments, including MLOps, CI/CD, automated testing, model or prompt evaluation, observability, monitoring, deployment, and operational support on at least one major cloud platform such as Azure, AWS, or GCP.Technical Profile - Required Strengths
- Design, implement, test, deploy, and maintain production-grade AI/ML and Generative AI solutions aligned with business and technical requirements.
- Build and integrate LLM-powered application capabilities, including summarization, Q&A, document processing, semantic search, automation, decision support, and workflow augmentation.
- Develop retrieval-augmented generation solutions using embeddings, chunking strategies, vector databases or vector search, semantic retrieval, and grounding techniques.
- Integrate hosted AI services and foundation models into enterprise applications through secure APIs, microservices, and cloud-native services.
- Develop and maintain backend services, APIs, pipelines, and automation using Python and related frameworks.
- Collaborate with product owners, architects, data engineers, DevOps engineers, business stakeholders, and client technical teams to translate requirements into working AI-enabled solutions.
- Contribute to prompt engineering, prompt template design, prompt chaining, response evaluation, and systematic improvement of AI outputs.
- Support AI observability and evaluation practices, including monitoring accuracy, groundedness, hallucination risk, response quality, latency, cost, drift, reliability, and operational behavior.
- Apply responsible AI practices, including privacy, security, compliance, bias awareness, fairness considerations, explainability, and appropriate handling of sensitive enterprise data.
- Participate in code reviews, architecture discussions, design reviews, sprint ceremonies, and technical decision-making.
- Support junior engineers through knowledge sharing, pairing, code review, and constructive technical guidance.
- Contribute to continuous improvement of engineering practices, including automation, testing, CI/CD, documentation, MLOps, and operational maturity.
Core Responsibilities
- AI/ML and Generative AI Engineering
- Hands-on experience building or integrating AI/ML and Generative AI capabilities in production or production-like environments.
- Experience with large language models and foundation models such as OpenAI GPT, Azure OpenAI, Anthropic Claude, Meta LLaMA, Google Gemini, Amazon Bedrock models, Hugging Face models, or comparable platforms.
- Practical understanding of LLM application patterns, including retrieval-augmented generation, semantic search, embeddings, vector search, prompt engineering, prompt templates and prompt chaining, tool calling or function calling, AI-assisted automation workflows, and human review or approval workflows where appropriate.
- Experience designing and implementing RAG pipelines that combine enterprise data, chunking, embedding generation, retrieval logic, and grounded response generation.
- Familiarity with vector databases or vector search technologies such as Azure AI Search, OpenSearch, FAISS, Pinecone, Weaviate, Milvus, Qdrant, pgvector, or similar tools.
- Ability to evaluate AI outputs for accuracy, groundedness, completeness, consistency, hallucination risk, prompt robustness, and business usefulness.
- Familiarity with AI evaluation and observability tools or practices such as MLflow, Ragas, DeepEval, LangSmith, Langfuse, Evidently, Arize Phoenix, OpenTelemetry, Prometheus, Grafana, or comparable approaches.
- Machine Learning and Data Science Foundations
- Solid understanding of machine learning fundamentals, including supervised and unsupervised learning, classification, regression, clustering, feature engineering, model evaluation, and common quality metrics.
- Familiarity with common ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face Transformers, NumPy, pandas, or similar tools.
- Ability to work with structured and unstructured data, including documents, text, metadata, logs, tabular data, and API-based data sources.
- Understanding of model lifecycle practices, including experimentation, training, evaluation, deployment, monitoring, retraining considerations, and versioning.
- Experience with NLP, document processing, text classification, information extraction, summarization, or search relevance is strongly preferred.
- Software Engineering Expertise
- Strong proficiency in Python for backend services, APIs, data processing, AI integrations, automation, and production-grade application components.
- Demonstrated ability to design clean, maintainable, well-tested codebases, including strong familiarity with unit testing, integration testing, and component-level test strategy.
- Strong understanding of software engineering fundamentals, including data structures and algorithms, API design and integration, RESTful services and microservices, modular and layered architectures, secure coding practices, performance, scalability, reliability, and maintainability considerations.
- Experience exposing AI or ML capabilities through APIs, microservices, batch jobs, pipelines, or user-facing applications.
- Familiarity with frameworks and tools such as FastAPI, Flask, Streamlit, Pydantic, SQLAlchemy, or similar Python-based application frameworks is preferred.
- Working knowledge of SQL and common database patterns for application and data workflows.
- Application Breadth
- Strong backend and integration capability is required, especially for AI-enabled services, APIs, pipelines, and enterprise application integration.
- Full-stack capability is preferred, especially where AI outputs are surfaced through user-facing applications, dashboards, approval interfaces, chat interfaces, or workflow tools.
- Experience collaborating with frontend engineers to support AI-driven user experiences, including response display, source attribution, confidence indicators, outlier flags, document previews, and feedback capture.
- Ability to contribute across end-to-end solution delivery, from data ingestion and AI service integration through API delivery, user experience integration, testing, deployment, and support.
DevOps, MLOps & Delivery Practices
- Strong experience with modern DevOps and MLOps practices, including CI/CD pipelines, automated testing and deployment, Infrastructure as Code, containerization, model, prompt, or pipeline versioning, experiment tracking or model registry practices, environment management, safe release and rollback practices, monitoring, logging, observability, and alerting.
- Familiarity with tools and platforms such as Git, GitHub, GitLab, Azure DevOps, Jenkins, Docker, Kubernetes, Terraform, Bicep, CloudFormation, AWS CDK, MLflow, Airflow, Databricks workflows, SageMaker Pipelines, Vertex AI Pipelines, Azure Machine Learning, or comparable ML lifecycle tooling.
- Ability to build or support reusable automation frameworks for AI application deployment, evaluation, monitoring, and regression testing.
- A mindset focused on operability, reliability, repeatability, and automation, not just prototype delivery.
- Cloud Platform Experience
- Strong experience with at least one major cloud platform: Azure, AWS, or GCP.
- Experience designing, deploying, or operating AI-enabled applications in cloud environments, including practical understanding of identity and access management, networking and security basics, managed compute and container services, managed databases and storage, monitoring and logging services, cost awareness and optimization, and secure access to enterprise data sources.
- Relevant AI services experience is strongly preferred, such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Azure Databricks, Azure App Services, Azure Functions, Azure Monitor, Application Insights, Amazon Bedrock, Amazon Q, SageMaker, Lambda, API Gateway, S3, DynamoDB, ECS, EKS, CloudWatch, OpenSearch, Google Vertex AI, Gemini, BigQuery, Cloud Run, GKE, Pub/Sub, Cloud Storage, Dataproc, Dataflow, or Cloud Monitoring.
- Depth in one major cloud platform is preferred over shallow exposure to many, though cross-cloud awareness is a plus.
AI Skills & Responsible Tool Usage
- Demonstrated experience using AI-assisted development tools to improve productivity, quality, testing, refactoring, documentation, and learning.
- Ability to validate AI-generated outputs rather than accepting them uncritically, including review for correctness, security, maintainability, licensing concerns, and hidden regressions.
- Understanding of enterprise responsible AI expectations, including sensitive data handling, privacy, intellectual property, policy compliance, transparency, and human oversight.
- Ability to explain benefits, limitations, risks, and trade-offs of AI-enabled solutions to both technical and non-technical stakeholders.
- Willingness to continuously upskill as AI tools, platforms, models, and enterprise governance practices evolve.
Professional Expectations
- Owns well-defined AI-enabled features end-to-end, contributes to technical decisions within scope, and escalates complex or ambiguous issues appropriately.
- Communicates clearly with both technical and non-technical stakeholders, including the ability to explain AI system behavior, limitations, risks, and business value.
- Balances innovation with pragmatic enterprise delivery, security, compliance, reliability, and maintainability.
- Comfortable working across multiple projects or domains as GE NA demand shifts.
- Collaborates effectively with architects, product owners, business stakeholders, data teams, DevOps teams, and client technical teams.
- Demonstrates consistent attention to code quality, documentation, testing, operational readiness, and responsible AI practices.
- Contributes positively to team culture, reusable patterns, knowledge sharing, and continuous improvement.
Experience & Education
- Typically 4+ years of professional software engineering, data engineering, machine learning engineering, or AI engineering experience, or equivalent demonstrated capability.
- Typically 1-2 years of hands-on experience with AI/ML, Generative AI, LLM integration, RAG, MLOps, or production AI-enabled application delivery.
- Strong professional experience with Python is required.
- Professional experience with at least one major cloud platform is required.
- Bachelors degree in Computer Science, Engineering, Data Science, Machine Learning, Artificial Intelligence, or a related field preferred, or equivalent practical experience.
- Relevant certifications are a plus but not required, including cloud, AI/ML, data engineering, DevOps, security, or Generative AI certifications.
Preferred / Nice-to-Have Skills
- Experience with agentic AI frameworks or orchestration patterns such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Microsoft Agent Framework, Google ADK, or comparable tools.
- Experience with multi-agent workflows, tool calling, function calling, workflow orchestration, planning agents, or AI-driven automation.
- Experience with data engineering technologies such as Spark, PySpark, Kafka, Airflow, Databricks, BigQuery, Snowflake, Delta Lake, Glue, Dataflow, or similar.
- Experience with document automation, enterprise search, knowledge management, workflow automation, or AI-enabled content generation.
- Experience with regulated industries such as healthcare, financial services, insurance, government, life sciences, or other compliance-sensitive environments.
- Experience with AI quality, evaluation, or testing practices, including golden datasets, benchmark evaluations, regression testing for prompts, prompt-injection testing, bias/fairness checks, and drift monitoring.
- Experience building reusable accelerators, templates, frameworks, or patterns that improve AI delivery speed and consistency across teams.
What This Role Is Not
- This role is not limited to research or experimentation; candidates should be able to deliver production-ready or production-adjacent AI solutions.
- This role does not require inventing new foundation models from scratch unless specifically required by a client engagement.
- This role is not purely prompt writing; candidates must understand software engineering, integration, testing, deployment, and operational support.
- This role is not purely data science; candidates should be able to integrate AI/ML capabilities into usable enterprise applications and workflows.
Job Type & LocationThis is a Permanent position based out of Austin, TX. Pay and BenefitsThe pay range for this position is $107500.00 - $161300.00/yr. Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.We reserve the right to pay above or below the posted wage based on factors unrelated to sex, race, or any other protected classification. Additional earnings may be available through incentive programs like annual bonuses, profit sharing, etc.
Our full-time, internal employment benefits include the following:
Medical, Dental, and Vision Critical Illness, Accident, and Hospital 401(k) Retirement Plan Pre-tax and Roth post-tax contributions available Life Insurance (Voluntary Life and AD&D for employee and dependents) Short and Long-Term Disability Health Spending Account (HSA) Transportation Benefits Employee Assistance Program Time Off/Leave (PTO, Vacation or Sick Leave) Job Type & LocationThis is a Permanent position based out of Austin, TX. Application DeadlineThis position is anticipated to close on Nov 24, 2026. Workplace TypeThis is a fully remote position.
About TEKsystems
We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company.
The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.
About TEKsystems and TEKsystems Global Services
Were a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. Were a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. Were strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. Were building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at TEKsystems.com.
The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.
San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records.
Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.
