AI Engineer - Agentic & Generative AI

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

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

AI coding tools - Claude Code, OpenAI Codex, GitHub Copilot, or similar

Vector database experience - Pinecone, Weaviate, ChromaDB, Qdrant, or equivalent

NLP fundamentals - tokenization, text classification, similarity, named entity recognition

Applied statistics and EDA experience beyond standard pandas workflows

Conversational AI or chatbot development experience

Cloud platform exposure - AWS, Azure, or GCP

Work on real enterprise AI problems across diverse client industries — not internal tooling or incremental maintenance

Grow fast — exposure to agentic AI, RAG, NLP, and data engineering within a single role alongside a senior team

Your contributions ship to real client deployments; ownership is real, not simulated

A team that values clean engineering and intellectual curiosity equally

Compensation competitive with current market standards for this level

Talent Acquisition Team

HR Department / SPAR Solutions

Address: SPAR Solutions India Pvt. Ltd.

Pune IT Park, B-503, Bhau Patil Marg, 34 Aundh Road,

Bopodi, Pune, Maharashtra, India.

Pin Code: 411020.

Website: sparsolutions.com

LinkedIn: https://www.linkedin.com/company/spar-solutions-llc

Twitter: https://twitter.com/sparsolutions

Facebook: https://www.facebook.com/SPARsolutions