AI/ML Engineer - LangChain/LangGraph

Description :

Role Summary :

The AI/ML Engineer will design, build, and operationalize scalable AI/ML solutions with a strong focus on LLM-based agentic systems. The role involves developing multi-agent workflows, retrieval-augmented generation (RAG) pipelines, and integrating large language models with enterprise systems while ensuring security, observability, and compliance.

This position works closely with product, platform, and MLOps teams to deliver production-grade AI solutions.

Key Responsibilities :

- Develop multi-agent systems with defined roles such as routers, supervisors, planners, and executors

- Manage state, memory, and context persistence across complex agent workflows

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models

- Integrate LLMs with external APIs, tools, and enterprise systems to enable task automation and decision support

- Develop reusable tools and agents for structured and unstructured data processing

- Implement CI/CD pipelines for LLM and AI applications

- Containerize AI workloads using Docker and orchestrate deployments using Kubernetes

- Deploy and manage AI solutions across cloud platforms such as AWS, Azure, and GCP

- Implement evaluation frameworks to measure accuracy, relevance, latency, and cost

- Enable observability using tracing, logging, and telemetry for LLM workflows

- Apply AI governance and guardrails, including compliance, content filtering, and responsible AI practices

- Apply security best practices, including prompt injection prevention, access controls, and data protection

- Optimize model performance, latency, and cost for production environments

- Design scalable architectures to support high-availability and enterprise workloads

- Develop and support NLP and Computer Vision solutions, including multimodal AI agents

- Collaborate on model selection, fine-tuning, and experimentation across text, image, and hybrid workloads

Required Qualifications and Skills :

- Deep understanding of multi-agent architectures, workflow orchestration (graphs, routers, supervisors), and memory management

- Experience with RAG architectures, vector databases, and embedding strategies

- Proficiency in Python and AI/ML libraries

- Strong knowledge of MLOps practices, CI/CD, Docker, Kubernetes, and cloud platforms

- Familiarity with evaluation, observability, and AI governance tools

Security and Optimization :

- Experience with performance tuning, scalability, and cost optimization for AI systems

Preferred Qualifications :

- Exposure to regulated or compliance-driven environments

- Contributions to open-source AI/ML or agent frameworks