Artificial Intelligence Engineer

Shakti Solutions

New York, NY, United States

about 2 months ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Application Performance Management Microsoft Azure Continuous Integration Software Debugging Memory Management Python (Programming Language) Redis Data Processing Istio Large Language Models Indexer

+6 more

Build Management Production Code Apache Kafka Software Version Control Key Vault Mulesoft

Job description

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.

Requirements

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.