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Shakti Solutions · Atlanta, GA

AI Engineer

seniorfull timePosted 14 days ago
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Stack mentioned

agentic-ailangchainragobservabilityazurehipaaredisapache-kafkaistioci/cdllmpythonanthropicopenaivector-databasesapi-design

Full-time or contract W-2 with Shakti Solutions - Visa-independent resources only.

Location: Chicago, IL / Atlanta, GA / NYC

Key Role Activities

- Design and build agentic systems for multi-step reasoning, planning, tool use, and workflow execution in regulated processes.

- Build stateful workflows with LangGraph/LangChain — branching, retries, self-correction, human-in-the-loop checkpoints.

- Engineer for reliability: error recovery, planning under uncertainty, robust handling of failed tool calls.

- Build auditable, policy-grounded reasoning for high-stakes decisions (e.g., prior authorization, claims review).

- Build RAG pipelines: ingestion, chunking, embeddings, retrieval, reranking, grounding.

- Manage conversational state, persistent memory, and context assembly; apply MCP-style tool/context interfaces.

- Implement observability and tracing (Azure Monitor/Application Insights) for prompts, tool calls, and agent behavior.

- Apply guardrails to reduce hallucinations and unsafe actions; evaluate agents at the task and trajectory level.

- Support PHI/HIPAA-aware data handling and human-oversight/escalation for regulated decisions.

- Integrate agents with enterprise systems and APIs (e.g., MuleSoft as an integration layer).

- Deploy and operate on Azure — AKS/ARO, Key Vault, Redis, Kafka, Istio, networking.

- Deliver production-quality code with strong testing, CI/CD, and documentation practices.

Required Qualifications

- Demonstrated production experience building agentic systems, not just exploration.

- Hands-on experience with LangGraph/LangChain or equivalent orchestration.

- Experience building end-to-end RAG systems: indexing, retrieval, reranking, grounding, evaluation.

- Solid understanding of context/memory management and retrieval-driven context assembly.

- Practical understanding of LLM limitations, hallucination risks, and evaluation methods.

- Experience debugging agent behavior at the trajectory/task level.

- Strong Python skills: testing, CI/CD, version control, API integration, production observability.

- Hands-on experience with at least one frontier model platform (Anthropic, Google, OpenAI).

- Working knowledge of Azure infrastructure — AKS/ARO, Key Vault, Redis, Kafka, Istio, networking.

- Clear communication and problem solving skills, ability to meet deadlines, plan work and pivot as needed, and work with a multidisciplinary, diverse team.

- Ability to travel 0–50% as needed.

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