AI Engineer

Role Summary We are hiring a Forward Deployed Engineer (FDE) / AI Engineer to design, build, deploy and operate production-grade agentic AI systems for real-time, AI-driven products. This is a hands-on, end-to-end role: you will own the solution from problem framing through full-stack build, cloud deployment and day-2 operations, working closely with business stakeholders as well as AI, ML and engineering teams. The role demands three strengths in combination — deep agentic and LLM engineering, the full-stack and cloud/DevOps ability to turn a model into a scalable application, and the business judgement to understand the underlying problem before proposing a solution. This is a full-time, on-site role based in Hyderabad.

Key Responsibilities
Agentic AI and LLM Engineering
• Architect, develop and integrate scalable agentic AI systems (autonomous and multi-agent, LLM-driven) into enterprise platforms and workflows.
• Design and evolve agent capabilities including planning, memory, RAG, tool usage, async task execution and orchestration.
• Build and tune retrieval pipelines end to end — chunking, embeddings, hybrid and semantic search, re-ranking, grounding and answer-quality evaluation.
• Evaluate emerging agentic models and frameworks; prototype, benchmark and optimise systems for performance, robustness, safety and cost. Full-Stack Solution Delivery
• Build the complete solution around the agent — APIs, backend services, data layer and clean, usable front-end interfaces.
• Move fast from working prototype to hardened production system without leaving throwaway architecture behind.
• Integrate with enterprise systems, authentication and existing platform components. Cloud, DevOps and Operations
• Own deployment and runtime: containerisation, CI/CD, infrastructure-as-code, environment and secrets management.
• Deploy and operate agentic systems in production; monitor behaviour, trace LLM calls, diagnose anomalies and implement feedback loops for continuous improvement.
• Engineer for scale and cost — latency, throughput, caching, token and inference spend. Business Partnering and Solutioning
• Engage business stakeholders directly to understand the problem, the process behind it and the value at stake before designing a solution.
• Frame solutions in business terms — define success metrics, quantify impact, and make pragmatic scope, build-versus-buy and sequencing trade-offs.
• Collaborate with AI engineers and engineering leadership to shape reliable, intuitive interactions with real-time AI systems. Candidate Profile Experience (Required)
• 5+ years of overall software engineering experience.
• 2+ years hands-on with LLMs / GenAI / Agentic AI in real, deployed applications — not experimentation alone.
• 1+ years of full-stack development experience.
• Hands-on experience with agent frameworks such as LangChain, LangGraph, OpenAI Agents SDK, Google ADK or similar.
• Experience building agentic systems, including chat or conversational interfaces, workflow-driven agents, tool use, and LLM-based protocols (e.g., MCP-style or equivalent).
• Proven track record building production AI agents with planning, memory, tool use, delegation and retrieval.
• Practical RAG experience with vector databases and semantic search (FAISS, Pinecone, Milvus, Weaviate, pgvector or similar).
• Working experience with at least one major cloud platform (AWS, Azure or GCP) and with DevOps practices for deploying and scaling services. Core Competencies
• Python and backend engineering: strong proficiency in Python and modern backend development (FastAPI, Flask or equivalent), including async and API design.
• Full-stack capability: REST and event-driven APIs, SQL and NoSQL data modelling, and a modern front-end framework (React or similar) — enough to ship a complete, usable application rather than a notebook.
• Cloud and DevOps: Docker, CI/CD pipelines, infrastructure-as-code, logging, monitoring and observability, and performance and cost tuning for LLM workloads.
• System design: strong architecture skills for scalable, resilient LLM applications, and the ability to communicate complex technical concepts clearly.
• Business lens: ability to understand the business problem and process, translate it into a technical solution, and articulate outcomes and value to non-technical stakeholders. This is a core requirement, not a nice-to-have.
• Ownership: comfortable with ambiguity; takes a problem statement through to a deployed, adopted solution. Good to Have
• Experience deploying agentic systems on platforms such as Vertex AI, Databricks or AWS Bedrock.
• Familiarity with agent safety, evaluation and governance (RLHF, guardrails, eval harnesses, monitoring).
• Background in distributed systems, performance-critical workloads or GPU-based systems.
• Exposure to Kubernetes, Terraform or equivalent orchestration and IaC tooling.
• Prior experience in a forward-deployed, customer-facing or solution-engineering role.