AI Engineer – Agentic, Generative AI

SPAR Solutions

Mid-level Software Engineer at SPAR Solutions developing agentic AI workflows and integrating LLMs. Collaborating on AI solutions across diverse industries in a hybrid work environment.

Posted 7/20/2026full-timePune • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and integrating Agentic AI workflows, implementing RAG pipelines, and utilizing LLMs through effective prompt engineering. Proficient in Python programming, test-driven development, and data analysis using relevant libraries and frameworks.

Highest-signal resume keywords

Python ProgrammingAgentic AI FrameworksRAG Pipeline ImplementationPrompt EngineeringData Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonLangChainLangGraphAutoGenCrewAIOpenAIAnthropicGeminiNumpyPandas

Soft Skills

Strong Written CommunicationStrong Verbal CommunicationAdaptabilityQuick Learner

Tools & Technologies

VS CodeGitMatplotlibSeabornPlotlyAgileScrumJIRA

Industry Keywords

RAG ConceptsMCP IntegrationsTest-Driven DevelopmentSOLID PrinciplesDesign Patterns

Tech Stack

Tools & technologies

NumpyPandasPythonSwitching

About the role

Key responsibilities & impact

Build and integrate Agentic AI workflows: tool use, memory, planning loops, and MCP integrations using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI
Implement RAG pipelines end-to-end: document ingestion, chunking, embedding, vector retrieval, and evaluation
Integrate LLMs via API: prompt engineering, function calling, structured outputs, and context management across providers such as OpenAI, Anthropic, and Gemini
Use AI coding agents (Claude Code, Codex, Copilot, or equivalent) as part of day-to-day development; direct and validate AI-generated output effectively
Write clean, maintainable, production-quality Python code following SOLID principles and established design patterns
Apply test-driven development practices; write and maintain unit and integration tests as a standard part of delivery
Participate in code reviews, Agile/Scrum ceremonies, and JIRA-driven delivery workflows
Work within Git-based version control; follow established branching, PR, and code review processes
Build data pipelines for ingestion, transformation, and analysis using Python, numpy, and pandas
Perform exploratory data analysis; generate charts, graphs, and visual summaries using Matplotlib, Seaborn, Plotly, or equivalent
Contribute to data quality assessment and transformation workflows as part of broader AI solution delivery

Requirements

What you’ll need

3 to 5 years of software engineering experience with strong, demonstrable Python fundamentals
Hands-on experience with at least one agentic AI framework - LangChain, LangGraph, AutoGen, CrewAI, or equivalent
Experience prompting and integrating at least one major LLM - OpenAI, Anthropic Claude, Google Gemini, or similar
Working knowledge of RAG concepts - chunking strategies, embeddings, vector stores, and retrieval evaluation
Familiarity with MCP integrations and agentic workflow patterns
Strong prompt engineering skills - structured, systematic, and iterative approach
Well versed in use of SOLID principles, common design patterns, and clean code practices
Test-driven development and unit testing experience
Proficiency with numpy, pandas, and at least one visualization library (Matplotlib, Seaborn, or Plotly)
Foundational understanding of ML concepts - how models are trained, data preparation, and the purpose of fine-tuning
VS Code or comparable IDE; Git version control
Agile/Scrum experience
Strong written and verbal communication - able to explain technical decisions clearly to non-technical stakeholders
Adaptable and quick to learn - comfortable switching across technology stacks and client domains

Benefits

Comp & perks

Competitive salary
Professional development opportunities