Live opening · Posted 15 hours ago

Associate Lead - AI/ML Engineer ( Python, AL/ML) - GB04

Bajaj Finserv · Pune Division, Maharashtra, India (On-site)
Linkedin No
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At a glance

The key details from the original listing.

Posted 15 hours ago
CompanyBajaj Finserv
LocationPune Division, Maharashtra, India (On-site)
Work modeNo
SkillsPython, AWS, Azure, GCP, Docker, Kubernetes, TensorFlow
SourceLinkedin
Listed15 hours ago

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About the role

Description supplied by the original job listing.

Roles & Responsibilities
• Contribute to the development and optimization of enterprise-wide search systems and models.
• Design and implement algorithms to improve indexing, query relevance, and search accuracy.
• Support taxonomy, ontology, and metadata model creation for better search outcomes.
• Collaborate with business units (Loans, Insurance, Investments) to build AI-enabled search features.
• Conduct analysis of user behavior and system metrics to refine search performance.
• Work with engineers, product managers, and designers to deliver integrated search solutions.
• Develop production-grade ML systems for ranking, personalization, and recommendations.
• Participate in proof-of-concept initiatives with internal and external partners.
• Follow best practices in software engineering including CI/CD, testing, and monitoring.
• Keep abreast of emerging developments in AI/ML to apply them in practical solutions.
Ideal Candidate
Strong AI Engineer / Machine Learning Engineer profiles.
Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
Mandatory (Age) - Candidate's Age should be below 28 Years
Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..
Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies
Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.
Experience: 3–6 years · Work from office · 5 days a week · Notice period up to 30 days
Benefits
Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of India’s most diversified Non-banking financial companies, and among Asia’s top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations we’re present in India.

Work arrangement
No

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