Associate Lead - AI/ML Engineer ( Python, AL/ML)
TalentXO · Pune · onsite · Posted 43d ago
Not disclosed3–5 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
About the Role
We are looking for a strong AI Engineer / Machine Learning Engineer to build and optimize enterprise search, ranking, recommendation, and AI-powered retrieval solutions across financial-services use cases.
Key Responsibilities- Develop and optimize enterprise search systems, indexing, ranking, and query relevance.
- Build AI/ML solutions for search, personalization, recommendations, and retrieval.
- Work on taxonomy, ontology, metadata, embeddings, and semantic search.
- Build RAG and knowledge-retrieval solutions using vector search and semantic retrieval.
- Analyze user behavior and system metrics to improve search accuracy and relevance.
- Collaborate with business, engineering, product, and design teams.
- Develop and deploy production-grade ML systems with CI/CD, testing, and monitoring.
- Drive POCs and emerging AI/ML initiatives.
Mandatory Requirements
- 3+ years of hands-on experience in AI / ML / Data Science / NLP / Deep Learning / GenAI.
- Strong Python, SQL, data analysis, feature engineering, and ML model development experience.
- Hands-on experience with PyTorch / TensorFlow / Keras / Scikit-learn or equivalent.
- Experience in NLP, embeddings, semantic search, recommendations, document understanding, or related AI/ML use cases.
- Hands-on experience with LLMs such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar.
- Proven experience with RAG, vector search, embeddings, chunking, indexing, and semantic retrieval.
- Strong experience with Git, CI/CD, production environments, and scalable ML systems.
- B.Tech/M.Tech from Tier-1 institutes – IITs, NITs, BITS.
- Age: Below 28 years.
- CTC structure: 75% Fixed + 25% Variable.
- MLflow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, MLOps/LLMOps.
- Vector Databases, Spark/PySpark, distributed ML pipelines, or real-time ML systems.
- Docker, Kubernetes, Azure/AWS/GCP, and cloud-native AI deployments.
- Experience in FinTech, Banking, Lending, Fraud/Risk Analytics, AI-first startups, Product, or SaaS companies.
Sourced from foundit
Skills
IndexingRetrieval Augmented Generationchunkingvector searchkerasNatural Language Processingfeature engineeringSemantic SearchEmbeddingsDocument UnderstandingLarge Language Models (LLMs)ML Model DevelopmentPytorchTensorflowScikit-Learnsemantic retrievalrecommendationsNLP embeddingsPythonScalable ML SystemsSQLProduction EnvironmentsDeep Learningdata analysisGenerative AiGITData ScienceArtificial IntelligenceMachine Learning