Lead- AI Engineer
Lead AI Engineer at Emplay designing and shipping production LLM-powered agents, RAG pipelines, and multi-agent orchestration on AWS. Day-to-day spans Python backend services (FastAPI/Flask), vector search (OpenSearch/Pinecone/pgvector), third-party CRM/API integrations, and owning CI/CD and cloud infrastructure reliability.
- Compensation
- ₹1.2M–₹1.5M / yr
- Location
- Bengaluru
- Work setup
- Remote
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 3-4 years of experience in AI/ML engineering or related technical role, preferably SaaS or cloud-native
- Hands-on experience with LLM APIs — AWS Bedrock, OpenAI, or Anthropic — building production GenAI features
- Strong knowledge of RAG pipeline design — embeddings, vector stores (OpenSearch, Pinecone, pgvector), retrieval strategies
- Proficiency with agent frameworks such as LangChain or LlamaIndex; familiarity with MCP
- Solid AWS skills — Lambda, ECS/EKS, S3, DynamoDB, OpenSearch, SQS, multi-region architecture
- Backend development in Python (FastAPI / Flask) for production services and APIs
- REST / GraphQL API design with OpenAPI documentation; experience with OAuth 2.0, SAML, OIDC
- Infrastructure-as-code with Terraform or AWS CDK; CI/CD with GitHub Actions or equivalent
- Strong problem-solving skills, able to work with ambiguous requirements
- Good communication skills with technical and non-technical stakeholders
Nice to have
- Experience with Sales Enablement or CRM integrations
- Familiarity with advanced agent orchestration and multi-tool LLM workflows
- Prior experience in a B2B SaaS or enterprise software environment
Full description
Compensation: ₹12L – ₹15L • No equity
Position: Lead- AI Engineer
Experience: 3-4 years
No of Position - 1
Location - Remote / Work from Home
Salary (CTC) - 12- 15 LPA
Role Overview
We are looking for a Lead- AI Engineer with hands-on experience in Generative AI, Retrieval-Augmented Generation (RAG), and AWS cloud services. You will work with teams to design and deliver production-grade LLM-powered features, build intelligent agents, and integrate enterprise tools — working closely with cross-functional teams to ship high-quality AI solutions at scale.
Key Responsibilities
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Design, build, and deploy LLM-powered agents and conversational AI features on AWS.
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Architect and optimise end-to-end RAG pipelines — embedding, vector indexing, and context retrieval.
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Implement Model Context Protocol (MCP) servers to support multi-agent orchestration workflows.
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Develop and maintain LLM evaluation frameworks for quality assurance and regression tracking.
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Build scalable, multi-region cloud infrastructure leveraging core AWS services.
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Secure APIs and enforce access controls, authentication flows, and data privacy standards.
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Integrate third-party platforms (CRM, Sales Enablement, analytics tools) via REST APIs and webhooks.
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Build bulk data ingestion/export pipelines and instrument usage analytics and logging.
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Own CI/CD pipelines, automated regression testing, and production reliability.
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Collaborate with product and engineering teams to scope, estimate, and deliver features on time.
Qualifications
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3-4 years of experience AI/ML engineering or a related technical role, preferably in SaaS or a cloud-native environment.
-
Hands-on experience with LLM APIs — AWS Bedrock, OpenAI, or Anthropic — and building production-ready GenAI features.
-
Strong knowledge of RAG pipeline design — embeddings, vector stores (OpenSearch, Pinecone, pgvector),and retrieval strategies.
-
Proficiency with agent frameworks such as LangChain or LlamaIndex; familiarity with MCP (Model ontext Protocol).Solid AWS skills — Lambda, ECS/EKS, S3, DynamoDB, OpenSearch, SQS, and multiregion architecture patterns.