Artificial Intelligence Engineer
NOTE: Candidates with 7+ years experience only apply, based in Pune and open to work from office with serving NP or below 60 days NP.
Position: Senior AI Engineer
Location: Viman Nagar, Pune, MH
Hybrid - (3 Work from office)
Start Date: ASAP/immediate joiner - 30 days
Shift Time: 4:30 pm to 1:30 am IST
Note: Candidates with 5+ yrs exp and above, and currently staying in Pune are considered only.
Job Overview
We are looking for a capable and agile Senior AI Engineer to join our team and accelerate our
AI-based developments. In this role, you will be responsible for the hands-on coding and
delivery of AI models and generative AI agentic systems.
We need a developer with strong backend foundations who can learn the Intuit AI ecosystem -
AI Workbench, quickly and translate business needs into production-ready capabilities. You will
embrace a modern, AI-native development workflow vibe coding using tools like Cursor and
Windsurf to rapidly prototype and deliver robust solutions.
Responsibilities
● Utilize next-generation AI coding assistants (Cursor, Windsurf) to accelerate the software development lifecycle, moving rapidly from ideation to production deployment
● Design and build AI agents and co-pilots that utilize Model Context Protocol (MCP) servers and tool-use architectures to automate complex workflows.
● Develop and maintain scalable Retrieval-Augmented Generation (RAG) pipelines and semantic search systems to provide high-quality grounding for AI models.
● Build backend services with AI at their core using Java and Python, ensuring scalability for millions of users and requests daily.
● Implement end-to-end features including prompt engineering, context design, fallback logic, and feature-level configuration.
● Discover, clean, and prepare data sources to refine data pipelines for model training, fine-tuning, and robust evaluation.
● Integrate AI systems with enterprise data sources, APIs, and internal platforms while maintaining strict security and privacy standards.
● Collaborate side-by-side with product managers, data scientists, and backend engineers to define success criteria and align model metrics with business goals.
● Create proofs-of-concept to explore new agent capabilities and frameworks, then harden successful ideas for production use.
● Design for reliability, observability, and cost efficiency in AI-powered systems operating at an enterprise scale.
Experience
● 5+ years of industry experience in software engineering.
● 2+ years of specific experience bringing AI models, LLM applications, or machine learning systems to production.
● Bachelors or Masters in Computer Science, Applied Math, Statistics, or equivalent practical experience.
Required Technical Skills
● Demonstrate expert proficiency in Python and Java, with the ability to manually architect, debug, and optimize complex backend services and distributed systems without reliance on external assistants.
● Exhibit proficiency with AI-assisted coding tools (Cursor, Windsurf) to enhance coding velocity and quality (Note: External requirement).
● Possess deep knowledge of Computer Science Fundamentals including data structures, algorithms, performance complexity, and memory tuning to write efficient, production-ready code
● Possess experience building agentic systems, multi-agent orchestration, or systems utilizing Model Context Protocol (MCP).
● Maintain deep knowledge of Large Language Models (LLMs), prompt lifecycle management, and fine-tuning techniques such as LoRA and QLoRA.
● Demonstrate implementation experience with Retrieval-Augmented Generation (RAG), vector databases, and semantic search.
● Apply knowledge of data query and data processing tools (i.e., SQL) and familiarity with processing platforms such as Pandas, NumPy, and Spark.
● Foundational AI Skills (Basic & Adopting)
● ML Fundamentals: Solid understanding of machine learning principles (training, validation) and statistical modelling techniques (classification, regression).
● Data Handling: Experience with data processing platforms (e.g., Pandas, NumPy, Spark) and SQL.
● Software Engineering Best Practices: Strong grasp of version control (Git), software design patterns, and writing clean, production-ready code.
● Bias for Action: Ability to operate in ambiguity and deliver practical AI solutions that drive measurable business impact.
● Rapid Ecosystem Adoption: Demonstrated ability to pick up new internal tools, cloud technologies (AWS), and AI infrastructure components quickly.
● Production Mindset: Understanding of AI observability, reliability, and cost-efficiency when operating at scale
If you are interested, please do share your updated resume to preeti.mane@aptino.com