Lead AI Engineer

Tekfortune is a fast-growing consulting firm specialized in permanent, contract & project-based staffing services for world's leading organizations in a broad range of industries. In this quickly changing economic landscape, virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps, our staffing experts can help you find the best job for you.


Role: Lead AI Engineer
Location: Chicago, IL, USA_ Onsite


Mandatory Skills: Generative AI
Other skills: AWS, Machine Learning, Cloud & Data Engineering


Years Of Experience: 8 to 15 Years

Job Description
Role Summary & Objectives

• Translate business automation and efficiency goals into scalable, production-grade AI solution.
• Design of autonomous multi-agent systems, complex reasoning loops, and tool-use workflows.
• Establish robust guardrails, human-in-the-loop decision controls, and system observability.

Key Responsibilities
1. Agent Orchestration
Define multi-agent collaboration patterns, memory management, and autonomous planning frameworks.
2. Governance & Security
Implement data privacy, compliance, risk mitigation, and evaluation guardrails across all AI touchpoints.
3. Cross-functional Leadership
Guide and mentor engineering teams, run discovery workshops with stakeholders, and define reusable deployment patterns.


Technical Stack & Expertise:
Generative AI & Agentic AI

• Retrieval-Augmented Generation (RAG) pipelines, semantic caching, and context window optimization.
• Function calling, tool use, and structured data extraction schemas.
• Evaluation metrics, tracing, and hallucination reduction guardrails.
• Designing agentic-first workflows and autonomous decision loops.

Multi-Agent Systems

• Multi-agent coordination patterns (supervisor-worker, decentralized collaboration, stateful graphs).
• Frameworks like LangChain, LangGraph, and Bedrock Core Runtime for state and memory management.
• Emerging interoperability standards like Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols.

Data & Vector Technologies

• Vector databases (e.g., Milvus, Amazon Aurora PostgreSQL) for high-speed similarity search.
• Data pipelines and embedding generation workflows using Python, FastAPI, or Apache Spark.

Cloud & Infrastructure

• Cloud-native deployment on AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.
• Containerization and orchestration using Docker and Kubernetes.
• CI/CD pipelines for automated testing of non-deterministic AI outputs.

Observability & Responsible AI

• Observability and logging pipelines for tracking agent token usage, latency, and failure states.
• Responsible AI frameworks, data privacy compliance, and bias mitigation guardrails.

Roles & Responsibilities

• Design and implement enterprise-scale Generative AI and Agentic AI solutions
• Develop and optimize RAG-based autonomous agent systems.
• Drive AI governance, security, compliance, and operational excellence.
• Collaborate with business and technical stakeholders to deliver scalable AI solutions.
• Mentor engineering teams and establish reusable deployment standards.

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