Hi ,
We are looking for the opportunites in Bangalore Location
If you are interested for the below skill set please send your updated details, profiles on below Email ID.
Ishijain1.ext@deloitte.com
Name
Contact
Email ID
Skills
Notice period
Current Company
Current CTC
Expected CTC
Total Experience
Relevant Experience
JD
Responsibilities:
- Leverage strong machine learning and data analysis experience in complex data environments to support business objectives in effective and scalable ways.
- Utilize a solid foundation in mathematics and statistics to guide model development, validation, and performance evaluation.
- Design and develop machine learning and LLM-based solutions, including applications such as RAG pipelines, chatbots, and intelligent assistants where applicable.
- Develop and integrate REST APIs and FastAPI-based services for model deployment and application integration.
- Support implementation of monitoring and observability practices for ML and LLM systems, including performance tracking and response evaluation.
- Build and maintain data pipelines, including data ingestion, preprocessing, transformation, and validation.
- Collaborate effectively within Agile Scrum teams, contributing to iterative development and continuous improvement.
- Document business processes, workflows, models, APIs, and technical implementations clearly and comprehensively.
- Engage with business and technical stakeholders to understand requirements and translate them into data science and AI solutions.
- Participate in collaborative conceptualization sessions to brainstorm and refine ML and LLM-based solutions.
- Assist in implementing agent-based workflows using modern frameworks and support integration with enterprise systems.
Mandatory Skills:
- Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
- Experience with data processing, cleansing, and validation to ensure data quality and integrity for analysis.
- Conduct data quality checks and exploratory data analysis to support model development.
- Demonstrated programming skills in Python and experience in building and integrating APIs.
- Build end-to-end machine learning models, including data preparation, feature engineering, and deployment workflows.
- Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.
- Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic approaches.
- Basic experience with Large Language Models such as GPT, BERT, or similar architectures.
- Understanding of prompt engineering and LLM application usage.
- Familiarity with vector databases, embeddings, and retrieval-based approaches.
- Basic understanding of agent frameworks such as LangChain or Semantic Kernel.
- Knowledge of data mining concepts and experience with data visualization tools and dashboards.
Preferred Skills:
- Experience working with Large Language Models and building simple LLM-based applications.
- Understanding of natural language processing (NLP) techniques and their application in business contexts.
- Familiarity with agent-based workflows and orchestration concepts.
- Basic knowledge of Model Context Protocol (MCP) or similar agent communication standards.
- Exposure to monitoring and observability practices for ML and LLM systems.
- Familiarity with RAG architectures and document retrieval pipelines.
- Exposure to advanced topics such as deep learning, transformers, and fine-tuning approaches.
- Ability to integrate ML and LLM solutions into larger product or business workflows.
- Identify opportunities to automate data processing, analysis, and business workflows using ML and LLM techniques.
- Propose hypotheses and conduct experiments to solve business problems using data-driven approaches.
Additional Responsibilities:
- Stay updated with the latest advancements in machine learning, LLMs, and AI tools.
- Contribute to team knowledge sharing and adoption of best practices in ML and LLM development.
- Support senior team members in building scalable, reliable, and efficient AI systems.
- Ensure data quality, consistency, and reliability across all data science workflows.
- Follow responsible AI practices, including fairness, explainability, and compliance standards.
