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Role Overview
Our Client is seeking an experienced Senior AI Engineer with deep expertise in LangChain and modern LLM frameworks. You will lead the development of advanced LLM-driven applications, design complex RAG architectures, and guide the team in best practices for LLM engineering.
Key Responsibilities
LangChain & LLM Development
Our Client is seeking an experienced Senior AI Engineer with deep expertise in LangChain and modern LLM frameworks. You will lead the development of advanced LLM-driven applications, design complex RAG architectures, and guide the team in best practices for LLM engineering.
Key Responsibilities
LangChain & LLM Development
- Design and develop advanced applications using LangChain, LangGraph, and LangSmith
- Build complex chains, agents, and multi-agent systems
- Implement custom tools, retrievers, and memory systems
- Develop context-aware conversational AI and chatbot applications
- Create workflow orchestration using LangGraph for complex reasoning tasks
- Integrate multiple LLM providers (OpenAI, Anthropic, Cohere, Azure OpenAI)
- Optimize prompt templates and output parsers
RAG & Vector Database Architecture
- Design and implement advanced Retrieval-Augmented Generation (RAG) systems
- Develop custom embedding strategies and chunking techniques
- Work with vector databases such as Pinecone, Weaviate, Chroma, Qdrant, Milvus
- Implement hybrid search (semantic + keyword) and reranking methods
- Build document loaders and text splitters for diverse data sources
- Optimize retrieval quality with metadata filtering and query expansion
- Develop multi-modal RAG systems (text, images, documents)
Advanced LLM Techniques
- Apply advanced prompt engineering: few-shot, chain-of-thought, ReAct
- Build function calling and tool-use patterns
- Develop structured output with Pydantic models
- Implement streaming responses and async pipelines
- Build efficient caching strategies for cost optimization
- Implement fallback mechanisms and robust error handling
- Integrate guardrails and content moderation systems
Production & Monitoring
- Deploy LangChain applications to production using LangServe
- Integrate LangSmith for tracing, debugging, and evaluation
- Implement monitoring and observability for LLM applications
- Build evaluation frameworks for quality assurance
- Optimize latency, cost, and token usage
- Run A/B testing for prompt variations
- Create dashboards to monitor key LLM metrics
Required Qualifications
Education
Education
- Bachelor’s degree in Computer Science, Machine Learning, or related field (required)
- Master’s degree in AI/ML or Computer Science (preferred)
Experience
- 5+ years of software development experience
- 3+ years working with AI/ML and LLM applications
- 2+ years hands-on experience with LangChain (mandatory)
- Proven experience building production-grade LLM applications
- Strong background with RAG architectures and vector databases
Technical Skills – LangChain Ecosystem
LangChain Core (Required)
LangChain Core (Required)
- Expert-level knowledge of LangChain (v0.1+)
- Deep understanding of Chains, Agents, Tools, Memory, Callbacks
- Experience with LangChain Expression Language (LCEL)
- Knowledge of LangGraph for complex workflows
- Familiarity with LangSmith for monitoring/debugging
- Experience deploying with LangServe
- Understanding of LangChain Templates
LLM Providers & APIs
- Strong experience with OpenAI API (GPT-4, GPT-3.5, embeddings)
- Knowledge of Anthropic Claude API
- Familiarity with Azure OpenAI Service
- Experience with open-source models (Llama, Mistral, Mixtral)
- Knowledge of Hugging Face Inference API
- Understanding model capabilities, limitations, and pricing models
Vector Databases & Embeddings
- Hands-on experience with Pinecone, Weaviate, or Chroma
- Knowledge of embedding models (OpenAI, Cohere, sentence-transformers)
- Understanding similarity search algorithms
- Experience with metadata filtering and hybrid search
- Knowledge of vector indexing strategies (HNSW, IVF)
Programming & Software Engineering
- Expert-level Python (asyncio, type hints, Pydantic)
- Experience with FastAPI
- Strong understanding of async/await patterns
- Strong software engineering fundamentals
- Experience with Git, CI/CD, testing frameworks
Cloud & Infrastructure
- Experience with AWS (Lambda, ECS, S3, DynamoDB) or Azure
- Docker containerization
- Kubernetes (preferred)
- Serverless architectures
- API Gateway and load balancing
Core Capabilities
- Deep understanding of LLM capabilities and limitations
- Expertise in prompt engineering and optimization
- Ability to design scalable LLM architectures
- Strong understanding of cost optimization for LLM workloads
- Knowledge of AI safety and responsible AI practices
- Excellent problem-solving and communication skills
Preferred Qualifications
- Contributions to LangChain or other open-source LLM projects
- Experience with LlamaIndex, Haystack, or similar frameworks
- Knowledge of fine-tuning (LoRA, QLoRA)
- Experience with multi-agent systems and AutoGPT patterns
- Knowledge of semantic caching and query optimization
- Experience with document intelligence and OCR
- Understanding transformer architectures
- Experience with evaluation frameworks (RAGAS, TruLens)
- Experience with streaming and real-time LLM processing
- Blog posts, tutorials, or presentations on LangChain
- Cloud certifications
What They Offer
- Highly competitive salary and equity package
- Full health, dental, and vision insurance
- Generous budget for professional development and certifications
- Flexible working arrangements (remote/hybrid options)
- Access to cutting-edge AI infrastructure and LLM APIs
- Collaborative environment with world-class AI professionals
- Participation in conferences and AI community events
- Opportunities for technical leadership and mentorship
- Influence over the company’s AI strategy
Success Metrics
Success in this role will be measured by:
Success in this role will be measured by:
- Quality and performance of LangChain applications
- Reduction in latency and improved cost efficiency
- Accuracy and relevance of RAG systems
- Adoption of best practices across the team
- Speed of delivery for new AI features
- User satisfaction with AI-powered features
- Technical leadership and mentorship impact
- Contribution to documentation and knowledge base
Technologies You Will Work With
LLM Frameworks
LLM Frameworks
- LangChain, LangGraph, LangSmith, LangServe
- OpenAI API, Anthropic Claude API
- Hugging Face Transformers
Vector Databases
- Pinecone, Weaviate, Chroma, Qdrant
- PostgreSQL with pgvector
Development Stack
- Python 3.11+, FastAPI, Pydantic
- Docker, Kubernetes
- AWS/Azure cloud services
- Git, GitHub Actions
Monitoring & Observability
- LangSmith
- Prometheus, Grafana
- DataDog, New Relic
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