AI/ML Engineer II

TEKsystems • Baltimore, MD

10d • $107,500 - $161,300 • Remote

About The Position

The Mid-Level AI/ML Engineer is a hands-on technical contributor who builds, deploys, and maintains production-grade AI/ML and Generative AI solutions for client and internal initiatives. The role combines software engineering, AI integration, data engineering, cloud delivery, and responsible AI. The engineer translates well-defined business requirements into secure, scalable, maintainable AI solutions; integrates AI capabilities into enterprise applications; evaluates model and system behavior; and supports operational maturity after deployment.

The role involves developing LLM-powered applications and retrieval-augmented generation (RAG) solutions, including embeddings, chunking, vector search, semantic retrieval, prompt engineering, and grounded response generation. It also includes backend services, APIs, data pipelines, cloud-native AI service integrations, testing, deployment, monitoring, evaluation, and operational support. The engineer collaborates with product owners, architects, data and DevOps engineers, business stakeholders, and client technical teams, and contributes to design decisions, code reviews, knowledge sharing, and continuous improvement.

The position is permanent and fully remote, based out of Baltimore, Maryland. The posted annual pay range is $107,500–$161,300. The position is anticipated to close on November 9, 2026. The employer describes the role as intermediate level and typically expects at least four years of relevant professional experience, including one to two years of hands-on AI/ML or Generative AI experience.

Requirements

• Typically 4 or more 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.
• Professional experience with at least one major cloud platform: Azure, AWS, or GCP.
• Hands-on experience building or integrating AI/ML and Generative AI capabilities in production or production-like environments.
• Experience with large language models or foundation models and practical understanding of LLM application patterns, including RAG, semantic search, embeddings, vector search, prompt engineering, prompt templates or chains, and tool or function calling.
• Experience designing and implementing RAG pipelines combining enterprise data, chunking, embedding generation, retrieval logic, and grounded responses.
• Strong software engineering fundamentals, including clean and maintainable code, testing, data structures and algorithms, API design, RESTful services or microservices, modular architecture, secure coding, performance, scalability, reliability, and maintainability.
• Demonstrated familiarity with unit, integration, and component-level testing strategies.
• Ability to work with structured and unstructured data, including documents, text, metadata, logs, tabular data, and API-based data sources.
• Solid understanding of machine learning fundamentals, including supervised and unsupervised learning, classification, regression, clustering, feature engineering, model evaluation, and common quality metrics.
• Understanding of model lifecycle practices, including experimentation, training, evaluation, deployment, monitoring, retraining considerations, and versioning.
• Experience with modern DevOps and MLOps practices, including CI/CD, automated testing and deployment, Infrastructure as Code, containerization, versioning, environment management, safe releases and rollbacks, monitoring, logging, observability, and alerting.
• Experience designing, deploying, or operating AI-enabled applications in cloud environments, with practical understanding of identity and access management, networking and security basics, managed compute, databases and storage, monitoring and logging, cost awareness, and secure access to enterprise data.
• Ability to evaluate AI outputs for accuracy, groundedness, completeness, consistency, hallucination risk, prompt robustness, and business usefulness.
• Understanding of responsible AI expectations, including sensitive data handling, privacy, intellectual property, policy compliance, transparency, and human oversight.
• Ability to validate AI-generated development outputs for correctness, security, maintainability, licensing concerns, and potential regressions.
• Clear communication and effective collaboration with technical and non-technical stakeholders and cross-functional teams.
• Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent practical experience; the degree is preferred.
• Comfort working across multiple projects or domains as demand shifts and balancing innovation with enterprise security, compliance, reliability, and maintainability.

Nice To Haves

• Experience with NLP, document processing, text classification, information extraction, summarization, or search relevance.
• Familiarity with ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face Transformers, NumPy, or pandas.
• Familiarity with vector databases or search technologies such as Azure AI Search, OpenSearch, FAISS, Pinecone, Weaviate, Milvus, Qdrant, or pgvector.
• Familiarity with AI evaluation and observability tools or practices such as MLflow, Ragas, DeepEval, LangSmith, Langfuse, Evidently, Arize Phoenix, OpenTelemetry, Prometheus, or Grafana.
• Familiarity with Python application frameworks and tools such as FastAPI, Flask, Streamlit, Pydantic, or SQLAlchemy.
• Full-stack capability, particularly experience surfacing AI outputs in applications, dashboards, approval interfaces, chat interfaces, or workflow tools.
• Experience collaborating with frontend engineers on response display, source attribution, confidence indicators, outlier flags, document previews, or feedback capture.
• Familiarity with development and delivery tools such as Git, GitHub, GitLab, Azure DevOps, Jenkins, Docker, Kubernetes, Terraform, Bicep, CloudFormation, AWS CDK, Airflow, Databricks workflows, SageMaker Pipelines, Vertex AI Pipelines, or Azure Machine Learning.
• Experience with relevant cloud AI services, such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Amazon Bedrock, SageMaker, Google Vertex AI, or Gemini.
• Experience with agentic AI frameworks or orchestration patterns such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Microsoft Agent Framework, or Google ADK.
• 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, or Dataflow.
• Experience with document automation, enterprise search, knowledge management, workflow automation, or AI-enabled content generation.
• Experience in regulated or compliance-sensitive industries such as healthcare, financial services, insurance, government, or life sciences.
• Experience with AI quality and testing practices, including golden datasets, benchmark evaluations, prompt regression testing, prompt-injection testing, bias and fairness checks, and drift monitoring.
• Experience building reusable accelerators, templates, frameworks, or patterns that improve AI delivery speed and consistency.
• Relevant cloud, AI/ML, data engineering, DevOps, security, or Generative AI certifications.
• Depth in one major cloud platform; cross-cloud awareness is a plus.

Responsibilities

• 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 capabilities such as summarization, question answering, document processing, semantic search, automation, decision support, and workflow augmentation.
• Develop RAG pipelines using enterprise data, chunking, embedding generation, retrieval logic, vector search, and grounded response generation.
• 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.
• Translate requirements into working AI-enabled solutions in collaboration with product owners, architects, data engineers, DevOps engineers, business stakeholders, and client technical teams.
• Design and improve prompts, templates, prompt chains, and AI responses; evaluate outputs for quality and business usefulness.
• Support AI evaluation and observability, monitoring accuracy, groundedness, hallucination risk, response quality, latency, cost, drift, reliability, and operational behavior.
• Apply responsible AI practices involving privacy, security, compliance, bias awareness, fairness, explainability, and sensitive enterprise data handling.
• Participate in code reviews, architecture and design discussions, sprint ceremonies, and technical decision-making.
• Support junior engineers through knowledge sharing, pairing, code reviews, and constructive technical guidance.
• Contribute to engineering improvements in automation, testing, CI/CD, documentation, MLOps, and operational maturity.
• Own well-defined AI-enabled features end-to-end, make technical decisions within scope, and escalate complex or ambiguous issues appropriately.
• Contribute to end-to-end solution delivery, including data ingestion, AI service integration, API delivery, user experience integration, testing, deployment, and support.
• Explain AI system behavior, limitations, risks, and business value to technical and non-technical stakeholders.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Critical illness, accident, and hospital coverage
• 401(k) retirement plan with pre-tax and Roth post-tax contributions
• Voluntary life and AD&D insurance for employees and dependents
• Short- and long-term disability insurance
• Health Spending Account (HSA)
• Transportation benefits
• Employee Assistance Program
• Paid time off, vacation, or sick leave
• Additional earnings may be available through annual bonus or profit-sharing incentive programs

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level