AI/ML Engineer

Job Role: AI/ML Engineer

Job Type: Permanent Full time

Location: Pune (Hybrid)

Notice Period: Immediate Joiners

Experience: 1.5 - 4 years

AI/ML Engineer

This role involves applying in-depth engineering principles, building scalable AI systems, experimenting with new GenAI technologies, and taking complete ownership of AI-driven initiatives.

Key Responsibilities

• Design, build, and deploy end-to-end ML and DL models for production use cases.
• Develop advanced GenAI solutions using Lang Chain, Lang Graph, Llama Index, vector databases (FAISS, Pinecone, Weaviate, etc.), and RAG-based architectures.
• Create Agentic AI workflows to automate reasoning and task execution.
• Build cloud-native AI pipelines (AWS/GCP/Azure), including model training, optimization, deployment, and monitoring.
• Work hands-on with data — sourcing, cleaning, preprocessing, and feature engineering.
• Develop backend components to integrate AI models with microservices and production systems.
• Rapidly prototype GenAI and ML features and convert research into deployable systems.
• Collaborate with engineering, product, and client teams to deliver reliable AI solutions.
• Continuously explore emerging ML/DL and GenAI technologies to improve internal capabilities.
  

Experience & Qualifications

Must Have:

• 2–4 years hands-on experience in Machine Learning and Deep Learning projects.
• Proven track record of building and deploying ML/DL models in real-world applications.
• Strong expertise in GenAI ecosystems:

Lang Chain

Lang Graph

RAG pipelines

Vector databases

Agentic AI workflows

• In-depth experience with Python, NumPy, Pandas, Scikit-learn, Py Torch or Tensor Flow
• Understanding of Transformers, LLMs, embeddings, and fine-tuning techniques.
• Solid knowledge of ML Ops practices (training pipelines, versioning, CI/CD for ML).
  

Good to Have:

• Experience with Snowflake or similar data platforms.
• Advanced Computer Vision model development.
• Cloud deployment expertise (AWS/GCP/Azure).
• Strong mathematical foundation: statistics, linear algebra, optimization.