Senior AI/ML Developer

About the Role We are looking for a highly skilled and experienced Senior AI/ML Engineer to join our team at eResearch Analysis. In this role, you will lead the design and deployment of production-grade AI/ML systems, drive innovation in Generative AI and agentic architectures, and build robust backend infrastructure that powers intelligent enterprise applications. Key Responsibilities Lead development of scalable backend services and APIs using Python (FastAPI, Django) Design, implement, and optimize end-to-end AI/ML pipelines and production-grade solutions Drive Generative AI development using RAG, vector stores, and LLM orchestration frameworks (LangChain, LlamaIndex) Architect and implement agentic AI systems and multi-agent workflows for enterprise automation Establish LLMOps practices: model versioning, evaluation frameworks, monitoring, and guardrails Collaborate with cross-functional teams to deliver robust, scalable features Conduct code reviews, mentor peers, and enforce engineering best practices Manage cloud deployments and monitor application performance across Azure, AWS, or GCP Ensure responsible AI practices including bias detection, explainability, and compliance Stay current with advances in LLMs, agentic AI, and ML infrastructure Participate in technical interviews and contribute to team growth Required Qualifications Bachelor's or Master's degree in Computer Science, AI/ML, or a related field 4+ years of experience in ML/AI development and 3+ years in Python backend/API development Deep expertise in Generative AI: RAG pipelines, vector databases, LLM APIs (OpenAI, Anthropic, Mistral, or equivalent) Proficient in ML frameworks: TensorFlow, PyTorch, or scikit-learn; experience with model fine-tuning is a plus Strong experience building RESTful APIs and implementing authentication/authorization mechanisms Solid database knowledge: SQL Server, PostgreSQL, MongoDB, and/or vector DBs (Pinecone, Weaviate, Qdrant) Hands-on experience with cloud platforms: Azure, AWS, or GCP Advanced debugging, profiling, and performance optimization skills Excellent communication skills Preferred Qualifications Experience with LLM orchestration frameworks: LangChain, LlamaIndex, CrewAI, or AutoGen Familiarity with agentic AI design patterns, multi-agent architectures, and tool-use protocols (MCP) Hands-on with LLMOps tools: MLflow, Weights & Biases, LangSmith, or similar Experience with Azure Synapse Analytics or distributed data querying Familiarity with Docker, Kubernetes, and CI/CD pipelines Knowledge of Elasticsearch for search and retrieval applications Experience with model fine-tuning techniques (LoRA, QLoRA, RLHF) on open-source LLMs Awareness of responsible AI principles: fairness, explainability, and governance Experience writing unit and integration tests; commitment to code reliability Power BI or equivalent data visualization skills (nice to have) Why Join Us Competitive compensation package Performance bonus and merit-based increments Fully Remote Work Flexibility with Work Hours Paid Time Off Collaborate with a forward-thinking, passionate engineering team Work on high-impact enterprise AI projects using modern tech stacks Culture of continuous learning, innovation, and knowledge sharing Location At present the job is fully remote. There is a plan to open a physical location in New Delhi. Heads up that in future this job will be converting into a Hybrid or full time on-site job. Job Type: Full-time Compensation: 2,000,000.00 - 2,800,000.00 per year Benefits: Paid time off Work from home Work Location: Remote About the Role We are looking for a highly skilled and experienced Senior AI/ML Engineer to join our team at eResearch Analysis. In this role, you will lead the design and deployment of production-grade AI/ML systems, drive innovation in Generative AI and agentic architectures, and build robust backend infrastructure that powers intelligent enterprise applications. Key Responsibilities Lead development of scalable backend services and APIs using Python (FastAPI, Django) Design, implement, and optimize end-to-end AI/ML pipelines and production-grade solutions Drive Generative AI development using RAG, vector stores, and LLM orchestration frameworks (LangChain, LlamaIndex) Architect and implement agentic AI systems and multi-agent workflows for enterprise automation Establish LLMOps practices: model versioning, evaluation frameworks, monitoring, and guardrails Collaborate with cross-functional teams to deliver robust, scalable features Conduct code reviews, mentor peers, and enforce engineering best practices Manage cloud deployments and monitor application performance across Azure, AWS, or GCP Ensure responsible AI practices including bias detection, explainability, and compliance Stay current with advances in LLMs, agentic AI, and ML infrastructure Participate in technical interviews and contribute to