Birlasoft: Associate Data Scientist – AWS AI

Job Description

The Associate Data Scientist – AWS AI role at Birlasoft in Bengaluru is focused on developing modern data and artificial intelligence solutions using AWS technologies, Python, SQL, large language models, and emerging generative AI architectures. The position is suited to professionals who want to work at the intersection of cloud data engineering, machine learning, and enterprise AI. Candidates will work with technologies such as AWS Glue, AWS Lambda, and Amazon Redshift to build and support data workflows while applying AI capabilities to practical business problems. The role also requires hands-on understanding of LLMs, Retrieval-Augmented Generation (RAG), Agentic AI, and prompt engineering, making it particularly relevant for professionals building expertise in the rapidly evolving generative AI and cloud data ecosystem.

A significant part of the position involves working with enterprise data and AI pipelines. AWS Glue and Lambda can be used to create scalable data-processing and serverless workflows, while Redshift provides the data warehousing foundation required for analytical workloads. Python and SQL are central technical skills for manipulating data, developing solutions, querying datasets, and integrating AI capabilities into applications. Beyond traditional data science, the role emphasizes generative AI concepts such as RAG architecture, which enables language models to retrieve relevant enterprise information before generating responses. Candidates with knowledge of Agentic AI can also contribute to solutions where AI systems use tools, data, and workflows to accomplish multi-step tasks. This combination creates an opportunity to work on practical, enterprise-focused AI applications rather than limiting the role to conventional analytics.

The role also provides exposure to newer AWS AI capabilities and productivity-focused AI tools. Knowledge of Amazon Bedrock can be particularly valuable for building applications around foundation models, while familiarity with Microsoft 365 Copilot or GitHub Copilot can help candidates understand how generative AI is being incorporated into workplace and software-development environments. Supply chain domain knowledge is considered an additional advantage and may help candidates connect technical AI solutions with real business processes. Overall, this opportunity is well suited to professionals who are curious about cloud computing, data science, generative AI, LLM applications, and intelligent automation. Birlasoft’s technology-focused environment can provide opportunities to strengthen practical skills while contributing to data-driven and AI-enabled enterprise solutions.

Roles & Responsibilities

1. Develop AWS Data Solutions
   Work with AWS services such as Glue and Lambda to develop scalable data-processing workflows, automation solutions, and cloud-based pipelines supporting enterprise AI and analytics initiatives.
2. Work with Amazon Redshift
   Use Redshift for data storage, querying, transformation, and analytical workloads while ensuring data is structured effectively for downstream reporting, analytics, and AI applications.
3. Build Python-Based Solutions
   Develop Python scripts, applications, utilities, and data-processing workflows that support data engineering, AI integration, automation, and enterprise application requirements.
4. Develop SQL Queries
   Write and optimize SQL queries for extracting, transforming, validating, and analyzing enterprise datasets while ensuring data quality and efficient processing.
5. Implement LLM Solutions
   Work with large language models to develop practical AI applications, experiment with model capabilities, and integrate generative AI into business and technology workflows.
6. Build RAG Architectures
   Design or contribute to Retrieval-Augmented Generation solutions that combine language models with enterprise data sources to produce more relevant, grounded, and context-aware AI responses.
7. Support Agentic AI Solutions
   Explore and implement AI agents capable of interacting with tools, data, and workflows to execute multi-step tasks and support intelligent business automation.
8. Apply Prompt Engineering
   Develop, test, refine, and evaluate prompts to improve the quality, consistency, relevance, and usefulness of responses generated by large language models.
9. Explore Amazon Bedrock
   Gain practical exposure to Amazon Bedrock and related AWS AI capabilities for developing, integrating, evaluating, and deploying generative AI applications.
10. Evaluate AI Productivity Tools
    Explore technologies such as Microsoft 365 Copilot and GitHub Copilot to understand their applications in workplace productivity, software development, automation, and enterprise AI.
11. Support Enterprise AI Integration
    Collaborate with technical and business stakeholders to understand requirements and translate them into practical cloud, data science, and AI-powered solutions.
12. Apply Domain Knowledge
    Use knowledge of business processes, particularly supply chain operations where applicable, to identify opportunities where data science, generative AI, and intelligent automation can deliver measurable value.

Requirements & Eligibility

1. Python Programming
   Strong practical knowledge of Python is important for developing data-processing scripts, AI applications, automation workflows, integrations, and supporting data science activities.
2. SQL Expertise
   Candidates should be comfortable writing SQL queries and working with structured datasets for analysis, transformation, validation, reporting, and AI-related data preparation.
3. AWS Glue Knowledge
   Hands-on experience with AWS Glue is mandatory for this role, particularly its use in data integration, transformation, ETL workflows, and cloud-based data processing.
4. AWS Lambda Experience
   Practical knowledge of AWS Lambda is required for developing serverless functions and event-driven workflows that can support automation and cloud-based data or AI applications.
5. Amazon Redshift
   Candidates should have hands-on knowledge of Redshift databases and understand fundamental concepts related to cloud data warehousing, querying, and analytical workloads.
6. Large Language Models
   Applicants should have practical exposure to LLMs and understand fundamental concepts surrounding model interaction, application integration, evaluation, and generative AI use cases.
7. RAG Architecture
   Knowledge of Retrieval-Augmented Generation is important, including the basic principles of retrieving relevant information and providing it as contextual input to language models.
8. Agentic AI
   Candidates should understand emerging Agentic AI concepts and how AI agents can use tools, workflows, APIs, and external information to accomplish multi-step objectives.
9. Prompt Engineering
   Practical understanding of prompt engineering is required, including the ability to design and refine prompts for better accuracy, relevance, consistency, and task completion.
10. Additional AI and Domain Skills
    Experience with Amazon Bedrock, Microsoft 365 Copilot, GitHub Copilot, or supply chain processes is considered advantageous and can strengthen a candidate’s profile for this role.

Expected Salary

For an Associate Data Scientist in Bengaluru, current market data indicates a broad compensation range depending on experience and specialization. Glassdoor reports approximately ₹7 LPA–₹13.3 LPA in base pay, with an average base pay around ₹10 LPA for Associate Data Scientist roles in Bangalore.

For this Birlasoft position, a realistic expectation would be around ₹5 LPA–₹9 LPA, particularly for an early-career candidate, based on Birlasoft Associate compensation data and comparable AI/data-science roles. Glassdoor has also listed a Birlasoft AI/ML Data Scientist position in Bengaluru at an estimated ₹5 LPA–₹7 LPA. The actual package can vary based on experience, AWS expertise, LLM/RAG skills, and the final level offered.

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