AI/ML Jobs

Role: AI Engineer - Agentic & Generative AI Experience: 3 to 5 years Education: Graduate - B.Tech/B.E. - Computers, Electronics/Telecommunication; PG - M.Sc. – Computers, M.Tech - Any Specialization, MCA - Computers Location: Pune. About SPAR Solutions SPAR Solutions is a software and AI consulting and services firm delivering enterprise-grade solutions across diverse client industries. Our AI practice spans agentic automation, Generative AI product development, RAG-powered knowledge systems, and data-driven analytics — across the full lifecycle from research and prototyping through production deployment. About the Role We are looking for a mid-level Software Engineer with a strong engineering foundation and hands-on Generative and Agentic AI experience. Software engineering fundamentals: Clean code , testability, design patterns, and Delivery discipline are the baseline. Agentic and Generative AI is the focus layer you bring on top. You will contribute directly to client engagements: building agentic pipelines, integrating LLMs, and delivering well-engineered solutions alongside a senior-leaning team. In a consulting environment, the ability to ramp quickly on new domains and technologies is as valuable as your core skill set. Key Responsibilities: Agentic AI Development 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 Software Engineering 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 Data & Analytics 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 Required 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 Desired: 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 Why SPAR Solutions 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 Contact Details: 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

Role: Senior AI Engineer - Agentic & Generative AI Experience: 10+ years Education:  Graduate - B.Tech/B.E. - Computers, Electronics/Telecommunication; PG - M.Sc. – Computers, M.Tech - Any Specialization, MCA - Computers Location:  Pune. About SPAR Solutions SPAR Solutions is a software and AI consulting and services firm delivering enterprise-grade solutions across diverse client industries. Our AI practice spans agentic automation, Generative AI product development, RAG-powered knowledge systems, and data-driven analytics — across the full lifecycle from research and prototyping through production deployment. About the Role: We are looking for a seasoned Software Engineer with hands-on Agentic and Generative AI experience to provide technical leadership on client engagements. This is a hands-on role - you will design solutions, write code, and deliver alongside the team while also setting technical direction, mentoring engineers, and engaging directly with client stakeholders. You will work heavily with AI coding agents to design and deliver solutions, which means strong prompt engineering discipline and the ability to direct AI-assisted development workflows are core to how this role operates. In a consulting environment, adaptability and the ability to bring structure to ambiguous problems are as important as technical depth. Key Responsibilities: Agentic AI Solution Design & Delivery Architect and deliver Agentic AI systems - multi-agent orchestration, tool use, memory, planning loops, and MCP integrations Design RAG pipelines and agentic workflows tailored to client knowledge and data landscapes Lead LLM integration strategy - model selection, prompt engineering, context management, and structured outputs across providers Use AI coding agents (Claude Code, Codex, Copilot, or equivalent) as a primary development tool; direct and validate AI-generated output with precision Technical Leadership & Client Engagement Provide technical leadership on engagements - own solution design, drive requirements gathering and analysis, and guide the team through delivery Lead client discovery sessions, technical scoping, and solution presentations; translate ambiguous business problems into well-defined AI solutions Conduct design reviews, enforce engineering standards, and mentor junior and mid-level engineers Drive technology selection; establish reusable patterns and internal frameworks that improve delivery across the team Data, Analytics & Engineering Apply statistical analysis and EDA to client data problems; generate charts, graphs, and visual summaries that communicate findings clearly to both technical and non-technical audiences Contribute to data quality assessment, pipeline design, and insight delivery using Python, numpy, and pandas Write clean, testable code following SOLID principles and design patterns; apply test-driven development practices and write unit tests as a standard part of delivery Practice Development Stay current on the Agentic AI landscape and bring relevant advances into SPAR's delivery practice Contribute to internal knowledge assets, accelerators, and capability building Required: 10+ years in software engineering with at least 1 year focused on Generative and Agentic AI Hands-on experience with at least one agentic framework - LangChain, LangGraph, AutoGen, CrewAI, or equivalent Direct experience prompting and integrating major LLMs - OpenAI, Anthropic Claude, Google Gemini, or similar Hands-on use of AI coding agents - Claude Code, OpenAI Codex, GitHub Copilot, or equivalent Experience with MCP integrations, agentic workflows, and multi-agent system design Strong prompt engineering skills - systematic, structured, and testable prompting practices Foundational understanding of ML concepts - how models are trained, data preparation, purpose and mechanics of fine-tuning, and when to apply pre-trained vs. fine-tuned models System design proficiency - distributed systems thinking, API design, scalability and reliability patterns Applied statistics and EDA - comfortable with data exploration, distributions, correlation, and translating findings into actionable insights Proficiency with Python, numpy, and pandas; experience with visualization libraries (matplotlib, seaborn, Plotly, or equivalent) Test-driven development practices and unit testing experience Experience leading technical workstreams or teams on client-facing consulting engagements Exceptional communication - equally effective whether writing a precise technical spec or presenting to a non-technical audience Agile methodology experience; comfortable owning delivery timelines and managing technical risk High adaptability - thrives across client domains and technology stacks; fast, self-directed learner Desired: Conversational AI and chatbot design experience Hands-on experience with data analytics and data science tooling beyond EDA Deeper ML experience - has built, trained, and evaluated models beyond conceptual understanding Transformer architecture depth - model behavior, failure modes, tokenization, and fine-tuning tradeoffs MLOps and model lifecycle management - experiment tracking, deployment pipelines, monitoring Multi-modal AI, knowledge graph construction, or domain-specific model adaptation Why SPAR Solutions Direct exposure to high-impact enterprise problems across industries Hands-on technical role with real leadership influence - you design, build, and set direction A fast-moving team where intellectual rigor and ownership are valued over hierarchy Opportunity to shape SPAR's AI practice - the frameworks and standards you establish become the team's foundation Compensation competitive with the current market Contact Details: 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