Lead I - Software Engineering - AI Solutions Analyst

Global Digital Transformation Solutions Provider

Lead I - Software Engineering - AI Solutions Analyst

Peak Hire Solutions

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Lead I - Software Engineering - AI Solutions Analyst

at Global Digital Transformation Solutions Provider

Agency job

via Peak Hire Solutions

6 - 8 yrs

₹10L - ₹26L / yr

Bengaluru (Bangalore), Mumbai, Delhi, Gurugram, Noida, Ghaziabad, Faridabad, Pune, Hyderabad

Skills

Large Language Models (LLM)

Prompt engineering

Knowledge base

Large Language Models (LLM) tuning

Artificial Intelligence (AI)

Interaction Design

Integration

Data integration

Integration testing

databricks

Amazon Web Services (AWS)

ChatGPT

MUST-HAVES:

LLM, AI, Prompt Engineering LLM Integration & Prompt Engineering
Context & Knowledge Base Design.
Context & Knowledge Base Design.
Experience running LLM evals

NOTICE PERIOD: Immediate – 30 Days

SKILLS: LLM, AI, PROMPT ENGINEERING

NICE TO HAVES:

Data Literacy & Modelling Awareness Familiarity with Databricks, AWS, and ChatGPT Environments

ROLE PROFICIENCY:

Role Scope / Deliverables:

Scope of Role Serve as the link between business intelligence, data engineering, and AI application teams, ensuring the Large Language Model (LLM) interacts effectively with the modeled dataset.
Define and curate the context and knowledge base that enables GPT to provide accurate, relevant, and compliant business insights.
Collaborate with Data Analysts and System SMEs to identify, structure, and tag data elements that feed the LLM environment.
Design, test, and refine prompt strategies and context frameworks that align GPT outputs with business objectives.
Conduct evaluation and performance testing (evals) to validate LLM responses for accuracy, completeness, and relevance.
Partner with IT and governance stakeholders to ensure secure, ethical, and controlled AI behavior within enterprise boundaries.

KEY DELIVERABLES:

LLM Interaction Design Framework: Documentation of how GPT connects to the modeled dataset, including context injection, prompt templates, and retrieval logic.
Knowledge Base Configuration: Curated and structured domain knowledge to enable precise and useful GPT responses (e.g., commercial definitions, data context, business rules).
Evaluation Scripts & Test Results: Defined eval sets, scoring criteria, and output analysis to measure GPT accuracy and quality over time.
Prompt Library & Usage Guidelines: Standardized prompts and design patterns to ensure consistent business interactions and outcomes.
AI Performance Dashboard / Reporting: Visualizations or reports summarizing GPT response quality, usage trends, and continuous improvement metrics.
Governance & Compliance Documentation: Inputs to data security, bias prevention, and responsible AI practices in collaboration with IT and compliance teams.

KEY SKILLS:

Technical & Analytical Skills:

LLM Integration & Prompt Engineering – Understanding of how GPT models interact with structured and unstructured data to generate business-relevant insights.
Context & Knowledge Base Design – Skilled in curating, structuring, and managing contextual data to optimize GPT accuracy and reliability.
Evaluation & Testing Methods – Experience running LLM evals, defining scoring criteria, and assessing model quality across use cases.
Data Literacy & Modeling Awareness – Familiar with relational and analytical data models to ensure alignment between data structures and AI responses.
Familiarity with Databricks, AWS, and ChatGPT Environments – Capable of working in cloud-based analytics and AI environments for development, testing, and deployment.
Scripting & Query Skills (e.g., SQL, Python) – Ability to extract, transform, and validate data for model training and evaluation workflows.
Business & Collaboration Skills Cross-Functional Collaboration – Works effectively with business, data, and IT teams to align GPT capabilities with business objectives.
Analytical Thinking & Problem Solving – Evaluates LLM outputs critically, identifies improvement opportunities, and translates findings into actionable refinements.
Commercial Context Awareness – Understands how sales and marketing intelligence data should be represented and leveraged by GPT.
Governance & Responsible AI Mindset – Applies enterprise AI standards for data security, privacy, and ethical use.
Communication & Documentation – Clearly articulates AI logic, context structures, and testing results for both technical and non-technical audiences.

MUST-HAVES:

LLM, AI, Prompt Engineering LLM Integration & Prompt Engineering
Context & Knowledge Base Design.
Context & Knowledge Base Design.
Experience running LLM evals

NOTICE PERIOD: Immediate – 30 Days

SKILLS: LLM, AI, PROMPT ENGINEERING

NICE TO HAVES:

Data Literacy & Modelling Awareness Familiarity with Databricks, AWS, and ChatGPT Environments

ROLE PROFICIENCY:

Role Scope / Deliverables:

Scope of Role Serve as the link between business intelligence, data engineering, and AI application teams, ensuring the Large Language Model (LLM) interacts effectively with the modeled dataset.
Define and curate the context and knowledge base that enables GPT to provide accurate, relevant, and compliant business insights.
Collaborate with Data Analysts and System SMEs to identify, structure, and tag data elements that feed the LLM environment.
Design, test, and refine prompt strategies and context frameworks that align GPT outputs with business objectives.
Conduct evaluation and performance testing (evals) to validate LLM responses for accuracy, completeness, and relevance.
Partner with IT and governance stakeholders to ensure secure, ethical, and controlled AI behavior within enterprise boundaries.

KEY DELIVERABLES:

LLM Interaction Design Framework: Documentation of how GPT connects to the modeled dataset, including context injection, prompt templates, and retrieval logic.
Knowledge Base Configuration: Curated and structured domain knowledge to enable precise and useful GPT responses (e.g., commercial definitions, data context, business rules).
Evaluation Scripts & Test Results: Defined eval sets, scoring criteria, and output analysis to measure GPT accuracy and quality over time.
Prompt Library & Usage Guidelines: Standardized prompts and design patterns to ensure consistent business interactions and outcomes.
AI Performance Dashboard / Reporting: Visualizations or reports summarizing GPT response quality, usage trends, and continuous improvement metrics.
Governance & Compliance Documentation: Inputs to data security, bias prevention, and responsible AI practices in collaboration with IT and compliance teams.

KEY SKILLS:

Technical & Analytical Skills:

LLM Integration & Prompt Engineering – Understanding of how GPT models interact with structured and unstructured data to generate business-relevant insights.
Context & Knowledge Base Design – Skilled in curating, structuring, and managing contextual data to optimize GPT accuracy and reliability.
Evaluation & Testing Methods – Experience running LLM evals, defining scoring criteria, and assessing model quality across use cases.
Data Literacy & Modeling Awareness – Familiar with relational and analytical data models to ensure alignment between data structures and AI responses.
Familiarity with Databricks, AWS, and ChatGPT Environments – Capable of working in cloud-based analytics and AI environments for development, testing, and deployment.
Scripting & Query Skills (e.g., SQL, Python) – Ability to extract, transform, and validate data for model training and evaluation workflows.
Business & Collaboration Skills Cross-Functional Collaboration – Works effectively with business, data, and IT teams to align GPT capabilities with business objectives.
Analytical Thinking & Problem Solving – Evaluates LLM outputs critically, identifies improvement opportunities, and translates findings into actionable refinements.
Commercial Context Awareness – Understands how sales and marketing intelligence data should be represented and leveraged by GPT.
Governance & Responsible AI Mindset – Applies enterprise AI standards for data security, privacy, and ethical use.
Communication & Documentation – Clearly articulates AI logic, context structures, and testing results for both technical and non-technical audiences.

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