Description

You will design, build, and deploy production-grade AI and ML systems, from data pipelines to model serving.

This role is on-site.

Responsibilities

• Develop and maintain scalable data processing pipelines using Spark and Databricks, handling batch and streaming workloads with Kafka.
• Build and optimize machine learning models, including classical supervised/unsupervised learning and deep learning architectures like transformers.
• Implement Generative AI solutions, including LLM integration, RAG systems, fine-tuning, and prompt engineering with appropriate guardrails.
• Design Agentic AI workflows involving tool-calling, reasoning loops, and multi-agent orchestration using frameworks like LangChain or AutoGen.
• Own the end-to-end delivery of ML features, including CI/CD automation, containerization, and secure model serving in production environments.

Required Skills

• Expert-level proficiency in Python and SQL with strong software engineering practices.
• Hands-on experience with PyTorch or TensorFlow for deep learning pipelines (CV/NLP).
• Practical knowledge of vector databases (FAISS, Pinecone, Weaviate) and search/retrieval systems (Elastic/OpenSearch).
• Experience with cloud platforms (AWS, GCP, or Azure) for training and serving (e.g., SageMaker, Vertex AI, AKS).
• Proficiency with CI/CD tools (GitHub Actions, GitLab CI), Docker, and Kubernetes orchestration.
• Familiarity with experiment tracking and model management tools like MLflow, Weights & Biases, or DVC.
• Understanding of data security, privacy, and governance, including handling PII and secure model serving.

Preferred Skills

• Bachelor's degree in Computer Science or related field with 4+ years of IT/ML experience.
• Experience with knowledge graphs and advanced RAG patterns.

Key Skills

Education

Bachelor's degree