Associate Lead – AI/ML Engineer (Python, AI/ML)

Associate Lead – AI/ML Engineer (Python, AI/ML)

Experience: 3–6 Years
Location: Pune
Work Model: Work from Office
Working Days: 5 Days
CTC: **₹19 LPA – ₹31.25 LPA(**The CTC structure will be as per company policy, with a fixed and variable component.)
Notice Period: Maximum 30 Days

About the Role

We are looking for a strong AI/ML Engineer to join our team and contribute to the development of enterprise-scale AI-powered search, recommendation, personalization, and knowledge retrieval solutions.

The ideal candidate should have strong hands-on experience in Python, Machine Learning, Deep Learning, NLP, LLMs, RAG, embeddings, semantic search, and production-grade ML systems.

Key Responsibilities

• Develop and optimize enterprise-wide search systems and machine learning models.
• Design algorithms to improve indexing, query relevance, search accuracy, ranking, and personalization.
• Build AI-enabled search and recommendation solutions.
• Work with NLP, embeddings, semantic search, text classification, and document understanding.
• Develop and implement RAG-based solutions and knowledge retrieval systems.
• Work with Large Language Models and foundation models.
• Analyze user behavior, model performance, and system metrics to improve AI/ML solutions.
• Develop production-grade and scalable ML applications.
• Collaborate with engineering, product, business, and design teams.
• Participate in proof-of-concept initiatives and evaluate emerging AI/ML technologies.
• Follow software engineering best practices including Git, CI/CD, testing, monitoring, and production deployment.

Mandatory Requirements

• 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI.
• Strong hands-on experience with Python and SQL.
• Strong knowledge of data analysis, feature engineering, model development, and production ML applications.
• Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent ML/DL frameworks.
• Experience with NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML applications.
• Hands-on experience with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or equivalent.
• Hands-on experience building or implementing RAG (Retrieval-Augmented Generation) systems.
• Experience with vector search, embeddings, chunking, indexing, knowledge retrieval, or semantic retrieval.
• Experience with Git, CI/CD, production environments, and scalable AI/ML systems.
• Candidate must be below 28 years of age.
• B.Tech / M.Tech from Tier-1 institutes such as IITs, NITs, or BITS.

Preferred Skills

• MLflow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry.
• MLOps / LLMOps frameworks.
• Vector Databases.
• Spark / PySpark.
• Distributed ML pipelines and large-scale data processing.
• Real-time ML systems.
• Docker and Kubernetes.
• AWS, Azure, or GCP.
• Experience designing scalable and cloud-native AI/ML architectures.

Preferred Industry Background

Candidates from the following backgrounds will be preferred:

• AI-first startups
• FinTech
• Banking
• Lending
• Fraud Analytics
• Risk Analytics
• Product Companies
• SaaS
• Data-driven Technology Companies

Interview Process

• Technical Round 1
• Technical Round 2
• Technical Round 3
• Underwriting Call
• Criminal Background Check

Important Eligibility Criteria

Candidates should meet the following mandatory criteria:

• 3–6 years of relevant experience
• Strong Python and AI/ML expertise
• Hands-on LLM and RAG experience
• Production-grade ML experience
• Age below 28 years
• B.Tech/M.Tech from IIT, NIT, or BITS
• Maximum notice period of 30 days
• Willingness to work from office in Pune

Pay: ₹1,900,000.00 - ₹3,125,000.00 per year

Application Question(s):

• Do you have 3+ years of hands-on AI/ML experience?
• Do you have strong hands-on Python and SQL experience?
• Do you have hands-on LLM experience?
• Did you complete B.Tech/M.Tech from IIT, NIT, or BITS?
• Have you built/implemented RAG systems?

Work Location: In person