Job
Description
About The Opportunity Join a fast-growing innovator in the Artificial Intelligence & Computer-Vision Solutions sector that builds production-grade GenAI and ML platforms for industrial automation, smart-city analytics, and digital inspection. Working on-site at our Pune & Nashik R&D hubs, you’ll transform cutting-edge research in generative vision models into reliable software and data pipelines that power real-world camera and sensor deployments. Role & Responsibilities Design, implement, and optimise end-to-end GenAI/ML use-cases—from data ingestion through model training, fine-tuning, and low-latency inference. Develop Python back-end services that integrate vision-centric LLMs and diffusion models into scalable REST/GraphQL or gRPC APIs. Embed ML training & inference workflows into Azure ML pipelines and schedule jobs across AKS clusters for high throughput and cost efficiency. Collaborate with Data Scientists to profile, prune, and quantise models for edge and cloud deployments, ensuring Build observability & automation tools (logging, alerting, auto-retraining triggers) to boost reliability, performance, and self-healing. Champion engineering excellence, enforcing code-review standards, Git branching strategy, and reusable CI/CD templates. Skills & Qualifications Must-Have 1–2 yrs professional experience in GenAI / ML projects with a focus on computer vision (classification, detection, segmentation). Strong Python development skills plus basic PySpark for data wrangling. Practical knowledge of Azure Machine Learning, Data Lake / Blob Storage, and SQL/NoSQL stores. Familiarity with model versioning, experimentation tracking, and MLOps best practices. Comfort with Git-based workflows and peer code reviews. Demonstrated ability to learn new frameworks rapidly and ship incremental value. Preferred Exposure to Azure Kubernetes Service (AKS) or any container orchestration for ML inference. Hands-on experience with vision-friendly GenAI frameworks (e.g., Segment-Anything, Stable Diffusion, CLIP). Knowledge of vector databases (FAISS, Milvus) for retrieval-augmented generation in vision tasks. Basic grasp of data pipeline schedulers (Airflow, Prefect) and monitoring stacks (Prometheus, Grafana). Familiarity with automated model-retraining and A/B testing in production. Participation in open-source vision or GenAI projects/hackathons. Skills: Gen AI,Python,Pyspark,Machine Learning,Azure Machine Learning,azure kubernetes services,azure cloud,git,azure data lake,source code management (git),azure blob storage,blob storage,sql Show more Show less
