Lead AI Engineer (Agentic Systems) (Hyderabad)

Role Summary

As the Lead AI Engineer (Agentic Systems), you willhelparchitect and build the organizations next generation of autonomous AI workflows. This is a multidisciplinary technical roleoperatingat the intersection of Software Engineering, Data Engineering, and Machine LearningEngineering. You will move beyond straightforward \"chatbots\" to design production-grade Agentic Systems: intelligent applications capable of reasoning, planning, and executing complex tasks autonomously.

Responsibilities

Agentic Systems Architecture Core Engineering

• Architect Build Multi-Agent Workflows: Lead the hands-on design and coding of stateful, production-grade agentic systems using Python and orchestration frameworks likeLangGraph,CrewAI, orAutoGen.
• Agent-to-Agent (A2A) Communication: Design and implement robust A2A protocols enabling autonomous agents to collaborate, hand off sub-tasks, and negotiate execution paths dynamically within multi-agent environments.
• State Management Orchestration: Engineer robust control flows for non-deterministic agents; implement complex message passing, memory persistence, and interruptible state handling to support long-running autonomous tasks.
• Tool Interface Design (MCP): Implement and standardize the Model Context Protocol (MCP) to create universal interfaces between agents, data sources, and operational tools, ensuring modularity and scalability.
• Model Integration Optimization:Utilizeproxy services (i.e.LiteLLM)to manage model routing and fallback strategies;optimizecontext windows and inference costs across proprietary and open-source models.
• Production Deployment: Containerize agentic workloads using Docker and orchestrate deployments on Kubernetes; leverage AWSAgentCoreor similar cloud-native services for scalable infrastructure.

Data Engineering Operational Real-Time Integration

• Build Agent Data Pipelines: Write andmaintainhigh-throughput ingestion pipelines (using Databricks or Python-based ETL) that transform raw operational signals into structured context for agents.
• Real-Time Context Injection: Ensure agents have access to \"operational real-time\" data (seconds/minutes latency) byoptimizingretrieval architectures and vector store performance.
• Cross-Functional Engineering: Act as the technical bridge between Data Engineering and AI teams; translate complex agent requirements into concrete data schemas and pipeline specifications, while stepping in to resolve hands-on bottlenecks in data availability.

Observability, Governance Human-in-the-Loop

• LLMOpsTracing: Implement comprehensive observability using tools likeLangfuseto trace agent reasoning steps,monitortoken usage, and debug latency issues in production.
• Safety Control Frameworks: Design hybrid execution modes ranging from Human-in-the-Loop (HITL) for sensitive operations to fully autonomous execution; build \"break-glass\" mechanisms and guardrails for automated decision-making.
• Evaluation Reliability:Establishtechnical standards for testing non-deterministic outputs; automate evaluation pipelines to measure agent accuracy, hallucination rates, and drift before deployment.

Technical Leadership Strategy

• Technical Roadmap Definition: Partner with Product and Engineering leadership to scope feasibility for autonomous projects; define the \"Agentic Architecture\" roadmap.
• Mentorship Standards: Define code quality standards, architectural patterns, and PR review processes for the AI engineering team; upskill team members on the latest agentic frameworks and methodologies.
• Innovation: Proactively prototype with emerging tools (e.g., new reasoning models, graph-based RAG) to solve high-value business problems, moving successful experiments into the production roadmap.

Qualifications

Required

• Experience: 7+ years of total technical experience in Software Engineering, Data Engineering, or Machine Learning.
• GenAI Specialization: 2+ years of specific experience building and deploying LLM-based applications or Agentic Systems in production.
• Database Lakehouse Mastery:Experience architecting storage layers for AI, including Vector Databases (e.g., Pinecone,Weaviate,Qdrant), NoSQL/Relational Databases (PostgreSQL, DynamoDB), and modern DataLakehouses(specifically Databricks or Snowflake).
• Cloud Infrastructure:Expertisein cloud architecture and container orchestration(AWS, GCP, or Azure)using Kubernetes and Docker. You must be comfortable deploying and scaling your own applications.
• LLM Ecosystem:Familiarity with common LLM frameworks and orchestration libraries (e.g.,LangGraph,LangChain,CrewAI,AutoGen). You understand the mechanics of RAG, embeddings, and context window management.
• Hybrid Engineering Skillset: A unique blend of Data Science (understanding model behavior, probability, and prompting) and Software Engineering (CI/CD, API design, asynchronous programming, and system reliability).
• Language Proficiency: Advancedproficiencyin Python for systems .