₹14L – ₹25L a year estimatetypical for IT Jobs in Pune, 56 posts
Posted 8 days ago Last checked 22 hr ago
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Role at a glance
Experience
8–12 years
Must-have skills
CI/CDEvent StreamingFastAPIGraphQL API DevelopmentKafkaKubernetes
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Job description
Location: Pune, India
Job Requirements Senior Engineer – Agentic AI Solutions & Presales (Grade G5) Grade / Level G5 (Senior Engineer) Experience 8 – 12 years Function Engineering – Agentic AI Platform, with Consulting & Presales Reports To Solution Architect Team Leads / mentors 2–3 junior engineers Location Pune – Preferred Role Summary We are looking for a hands-on senior engineer to help build and scale Agentic AI systems & our agentic AI delivery platform. You will work directly under a Solution Architect to implement the platform's agent, workflow, and data layers, while guiding 2–3 junior engineers on day-to-day build tasks. Alongside delivery work, you will support Consulting & Presales — building POCs, shaping technical proposals, and presenting solutions directly to customers. This is a hybrid builder + client-facing role, not a pure architecture or pure presales position. Key Responsibilities
Build core platform components — microservices (FastAPI), agent workflows, RAG pipelines, and integrations — working closely with the Solution Architect on design decisions.
Guide and review the work of 2–3 junior engineers; unblock them on implementation issues and enforce coding/design standards.
Translate customer requirements into technical POCs and MVPs; demo working solutions to prospects and clients.
Support presales: solution scoping, effort estimation, technical proposal/RFP inputs, and architecture walkthroughs in client conversations.
Stay current with the fast-moving agentic AI and LLM landscape and bring relevant patterns/tools back into the platform.
Troubleshoot issues across the stack — from agent logic down to infra/data layer — during build and pilot phases. Must-Have Skills
8–12 years in software engineering, including at least 1.5–2 years of hands-on work building LLM-powered or agentic applications (RAG, agent orchestration, tool/function calling, MCP, or similar — exact frameworks used may vary by past employer).
Strong Python and experience building/maintaining microservices (FastAPI or equivalent).
Practical exposure to at least one workflow orchestration engine (Temporal, Airflow, Camunda, or similar) — deep Temporal expertise is a plus, not a prerequisite.
Working knowledge of vector and/or graph data stores (e.g., Qdrant, Pinecone, pgvector, Neo4j) and event streaming (Kafka or equivalent).
Comfortable with containers/Kubernetes (any flavor — k3s, EKS, AKS, GKE) and standard CI/CD practices.
LLM ecosystem familiarity: multi-provider usage (OpenAI, Anthropic, Bedrock, or Vertex AI), prompt/agent evaluation basics, and awareness of observability tooling for LLM apps (Langfuse, Phoenix, LangSmith, or similar).
Prior exposure to presales/POC work — scoping, estimating, and demoing technical solutions to customers.
Strong verbal and written communication; confident presenting to client stakeholders, including technical and semi-technical audiences.
Experience informally leading or mentoring a small engineering team (2–3 engineers). Good to Have
Exposure to API gateways/service mesh (Kong, Linkerd, Istio) and identity/auth stacks (Keycloak, Ory, or similar).
Background in IT services / consulting delivery model.
Familiarity with GraphQL, hybrid search (vector + keyword + graph), or SRE/DevOps automation use cases.
Prior contribution to internal accelerators, reusable frameworks, or platform tooling.
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