Lead- AI Engineer
Summary
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: ₹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.
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Hands-on experience with LLM APIs — AWS Bedrock, OpenAI, or Anthropic — and building production-ready GenAI features.
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Strong knowledge of RAG pipeline design — embeddings, vector stores (OpenSearch, Pinecone, pgvector),and retrieval strategies.
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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.
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Backend development in Python (FastAPI / Flask) — able to write and maintain production-quality services and APIs.
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REST / GraphQL API design with OpenAPI documentation; experience with OAuth 2.0, SAML and OIDC.
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Infrastructure-as-code experience using Terraform or AWS CDK; CI/CD pipelines with GitHub Actions or equivalent.
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Strong problem-solving skills and ability to work with ambiguous requirements.
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Good communication skills, able to work with both technical and non-technical stakeholders.
Preferred Skills
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Experience with Sales Enablement or CRM integrations
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Familiarity with advanced agent orchestration and multi-tool LLM workflows.
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Prior experience in a B2B SaaS or enterprise software environment
Skills
- AI
- Analytics
- Anthropic
- API
- Api Design
- Authentication
- AWS
- AWS Bedrock
- CDK
- CI/CD
- Cloud
- Cloud Native
- Conversational AI
- CRM
- Data Ingestion
- DynamoDB
- ECS
- EKS
- Embeddings
- FastAPI
- Flask
- Generative AI
- GitHub
- GitHub Actions
- GraphQL
- Infrastructure as Code
- Lambda
- LangChain
- LlamaIndex
- LLM
- Machine Learning
- MCP
- OAuth
- OpenAI
- OpenAPI
- OpenID
- OpenSearch
- pgvector
- Pinecone
- Python
- RAG
- REST
- S3
- SaaS
- Sales Enablement
- SAML
- SQS
- Terraform
- Webhooks