Senior AI/ML Developer JD / Skills:
Senior AI/ML Engineer – GenAI, LLM, MLOps, Production ML Systems
About the Role
We are looking for a Senior AI/ML Engineer to architect and deploy production-grade AI/ML and Generative AI systems across domains such as NLP, Computer Vision, and deep learning.
This role requires strong expertise in the end-to-end ML lifecycle, LLM/RAG pipelines, scalable backend systems, and MLOps/LLMOps.
You will build scalable, reliable, and cost-efficient AI platforms used in real-world applications.
Key Responsibilities
- Design and implement end-to-end ML pipelines
- (data ingestion → preprocessing → training → deployment → monitoring)
- Build LLM-powered applications using RAG and agentic workflows
- Develop scalable ML/LLM microservices (FastAPI or similar)
- Implement MLOps/LLMOps best practices
- Optimize latency, throughput, and cost of AI systems
- Work with vector databases for semantic search and retrieval
- Design batch and real-time inference systems
- Collaborate with data engineering for robust data pipelines
- Lead AI system architecture decisions
- Mentor junior engineers and enforce production-quality standards
Tech Stack
Programming & Core
- Python (production-grade)
- JavaScript, React JS(Bonus)
- Strong SQL
AI/ML & Deep Learning
- PyTorch / TensorFlow
- Scikit-learn
- Hugging Face ecosystem
- Model evaluation & optimization tools
- Applied Mathametics for Machine Learning
Generative AI / LLM
- Gen AI Orchestration frameworks (LangChain , Langgraph , LlamaIndex , CrewAI Autogen or equivalent)
- Embeddings pipelines
- LLM evaluation frameworks
- Prompt orchestration systems
- Agentic workflow frameworks (preferred)
- Vector Databases
MLOps / LLMOps
- MLflow , Langsmith etc.
- Model registry & versioning
- CI/CD for ML (GitHub Actions, GitLab CI, etc.)
- Data validation (Great Expectations or similar)
- Model monitoring & observability
- Opentelemetry, Grafana
Backend & Systems
- FastAPI / Flask / Django
- Async Python
- Microservices architecture
- REST/gRPC APIs
- Docker
- Kubernetes
- Message queues (Kafka / RabbitMQ – bonus)
Cloud & Infrastructure (at least one)
- AWS (SageMaker, ECR, ECS/EKS, Lambda, S3)
- GCP (Vertex AI, GKE, Cloud Run, BigQuery)
- Azure ML (preferred)
- GPU-based deployment and optimization
Pay: Up to ₹1,000,000.00 per year
Benefits:
- Leave encashment
- Provident Fund
Ability to commute/relocate:
- Ahmedabad, Gujarat (Ahmedabad): Reliably commute or planning to relocate before starting work (Preferred)
Experience:
Work Location: In person
