Complete job description : -
Strong experience in designing and deploying Machine Learning, AI and Agentic AI applications in financial services.
Programing skills will be Python, SQL, R, SAS, PySpark, PostgreSQL, MySQL, MongoDB, NoSQL.
Should have excellent experience in Python, SQL, PySpark, TensorFlow, AWS SageMaker, Amazon Bedrock, NLP, LLMs, Agentic AI, and MLOps to build intelligent AI applications, autonomous agent workflows, multi-agent systems, and enterprise automation solutions.
Should have good experienced in Prompt Engineering, RAG Pipelines, AI Agent Orchestration, Model Deployment, Inference Pipelines, CI/CD, and Production-Grade AI Systems, supporting enterprise automation and decision-making initiatives.
Must have hands-on expertise with LLMs, AI Agents, LangChain, LangGraph, Function Calling, Vector Databases, REST APIs, MLflow, Docker, and Kubernetes.
Candidate should have good experience in Tableau, Power BI, Rest APIs and CI/CD Pipelines.
Must have strong experience in Cloud Platforms like AWS Bedrock, SageMaker, S3, EC2, Lambda and Azure - ML Studio, Databricks & Google Cloud Platform Mandatory experience in large data processing methods . Such as Data Analysis, Predictive Modeling, Feature Engineering, Data Preprocessing, ETL Pipelines, Apache Spark, Hadoop, Kafka, Hive, Databricks, Data Modeling.
Candidate should have deep Learning, NLP & Agentic AI frame works . Such as Transformer Models, BERT, GPT, LLMs, AI Agents, MCP, A2A, LLM Evaluation, Prompt Engineering, RAG Pipelines, LangChain, Hugging Face Transformers, Fine-Tuning, LangGraph, Function Calling.
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