Associate Lead/Engineer - AI/ML

Role : Strong AI Engineer / Machine Learning Engineer profiles.

Mandatory Requirements :

- 1. Experience : Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

- 2. Technical Skills : Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

- 3. Frameworks : Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

- 4. AI/ML Use Cases : Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

- 5. Large Language Models : Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

- 6. RAG Systems : Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

- 7. Engineering Practices : Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

- 8. CTC : The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

- 9. Age : Candidate's Age should be below 30 Years.

- 10. Pedigree : B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.

Preferred Requirements :

- 1. MLOps/LLMOps : Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

- 2. Distributed Systems : Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems.

- 3. Cloud & Infrastructure : Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

- 4. Industry Background : Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies.