Senior AI/ML Engineer
Role Overview
Seeking a Senior AI/ML Engineer comfortable with production systems and model development.
Architect agentic AI systems, generative AI applications, and ML pipelines for the core platform.
Own the ML project lifecycle, from statistical modeling and feature engineering through API deployment and production monitoring.
Work spans LLM orchestration, RAG pipelines, multimodal AI, and MLOps infrastructure.
Key Skills for This Role
Full Job Posting
Role overview
Seeking a Senior AI/ML Engineer comfortable with production systems and model development.
Architect agentic AI systems, generative AI applications, and ML pipelines for the core platform.
Own the ML project lifecycle, from statistical modeling and feature engineering through API deployment and production monitoring.
Work spans LLM orchestration, RAG pipelines, multimodal AI, and MLOps infrastructure.
Workplace
- The role is on-site at the Ahmedabad office.
- Work closely with product and engineering teams; architectural decisions shape how AI is built, scaled, and operated.
Must-have requirements
- Expert-level Python proficiency focused on clean, testable, efficient code.
- In-depth experience with TensorFlow and PyTorch, plus strong ML fundamentals including statistical modeling, classical algorithms, and evaluation metrics.
- Practical experience with LLMs such as GPT, Claude, or LLaMA; prompt engineering; and agentic frameworks such as LangChain or LangGraph.
Good to have
- Hands-on experience with vector databases such as Qdrant, Pinecone, or Weaviate and semantic search optimization.
- Extensive FastAPI experience building high-performance production microservices.
- Experience with experiment tracking and model monitoring platforms such as MLflow, Weights & Biases, or similar MLOps tools.
Responsibilities
- Design, develop, and deploy production-grade ML models and generative AI solutions across LLMs, RAG, and multimodal AI.
- Build and optimize AI agents using frameworks such as LangChain, LangGraph, and LangSmith to automate complex business workflows.
- Architect and maintain scalable AI pipelines and RESTful services using FastAPI and Python; implement model versioning, monitoring, and retraining practices.
- Implement RAG pipelines with vector databases and perform PEFT/LoRA fine-tuning on foundational models.
- Conduct advanced statistical analysis and feature engineering on large structured and unstructured datasets.
- Design and optimize storage solutions across SQL databases such as PostgreSQL and MySQL, and NoSQL databases such as MongoDB.
- Use software design patterns and robust data structures to make code maintainable, repeatable, and efficient.
- Lead code reviews, mentor junior engineers, and advocate for engineering best practices across AI initiatives.
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