Sr. AIML Developer

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:

AIML: 1 year (Preferred)

Work Location: In person