{"id":1947958,"url":"https://alion.io/job/aptino-artificial-intelligence-engineer","title":"Artificial Intelligence Engineer","company":{"id":3843294,"name":"Aptino","domain":"aptino.com","url":"https://alion.io/company/aptino","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Cursor","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tool Use","optional":false},{"name":"Windsurf","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-06T04:26:38Z","employer_posted_date":null,"last_verified_at":"2026-10-06T04:26:38Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"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.\n\nPosition: Senior AI Engineer\nLocation: Viman Nagar, Pune, MH \nHybrid - (3 Work from office)\nStart Date: ASAP/immediate joiner - 30 days\nShift Time: 4:30 pm to 1:30 am IST\nNote: Candidates with 5+ yrs exp and above, and currently staying in Pune are considered only. \n\nJob Overview \nWe are looking for a capable and agile Senior AI Engineer to join our team and accelerate our\nAI-based developments. In this role, you will be responsible for the hands-on coding and\ndelivery of AI models and generative AI agentic systems.\n\nWe need a developer with strong backend foundations who can learn the Intuit AI ecosystem -\nAI Workbench, quickly and translate business needs into production-ready capabilities. You will\nembrace a modern, AI-native development workflow vibe coding using tools like Cursor and \nWindsurf to rapidly prototype and deliver robust solutions.\n\nResponsibilities \n● Utilize next-generation AI coding assistants (Cursor, Windsurf) to accelerate the software development lifecycle, moving rapidly from ideation to production deployment\n● Design and build AI agents and co-pilots that utilize Model Context Protocol (MCP) servers and tool-use architectures to automate complex workflows.\n● Develop and maintain scalable Retrieval-Augmented Generation (RAG) pipelines and semantic search systems to provide high-quality grounding for AI models.\n● Build backend services with AI at their core using Java and Python, ensuring scalability for millions of users and requests daily.\n● Implement end-to-end features including prompt engineering, context design, fallback logic, and feature-level configuration.\n● Discover, clean, and prepare data sources to refine data pipelines for model training, fine-tuning, and robust evaluation. \n● Integrate AI systems with enterprise data sources, APIs, and internal platforms while maintaining strict security and privacy standards.\n● Collaborate side-by-side with product managers, data scientists, and backend engineers to define success criteria and align model metrics with business goals.\n● Create proofs-of-concept to explore new agent capabilities and frameworks, then harden successful ideas for production use.\n● Design for reliability, observability, and cost efficiency in AI-powered systems operating at an enterprise scale.\n\nExperience \n● 5+ years of industry experience in software engineering.\n● 2+ years of specific experience bringing Agentic AI models, LLM applications, RAG & machine learning systems to production.\n● Bachelors or Masters in Computer Science, Applied Math, Statistics, or equivalent practical experience.\n\nRequired Technical Skills \n● 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.\n● Exhibit proficiency with AI-assisted coding tools (Cursor, Windsurf) to enhance coding velocity and quality (Note: External requirement).\n● Possess deep knowledge of Computer Science Fundamentals including data structures, algorithms, performance complexity, and memory tuning to write efficient, production-ready code\n● Possess experience building agentic systems, multi-agent orchestration, or systems utilizing Model Context Protocol (MCP). \n● Maintain deep knowledge of Large Language Models (LLMs), prompt lifecycle management, and fine-tuning techniques such as LoRA and QLoRA. \n● Demonstrate implementation experience with Retrieval-Augmented Generation (RAG), vector databases, and semantic search.\n● Apply knowledge of data query and data processing tools (i.e., SQL) and familiarity with processing platforms such as Pandas, NumPy, and Spark. \n● Foundational AI Skills (Basic & Adopting)\n● ML Fundamentals: Solid understanding of machine learning principles (training, validation) and statistical modelling techniques (classification, regression).\n● Data Handling: Experience with data processing platforms (e.g., Pandas, NumPy, Spark) and SQL.\n● Software Engineering Best Practices: Strong grasp of version control (Git), software design patterns, and writing clean, production-ready code.\n● Bias for Action: Ability to operate in ambiguity and deliver practical AI solutions that drive measurable business impact.\n● Rapid Ecosystem Adoption: Demonstrated ability to pick up new internal tools, cloud technologies (AWS), and AI infrastructure components quickly.\n● Production Mindset: Understanding of AI observability, reliability, and cost-efficiency when operating at scale\n\nIf you are interested, please do share your updated resume to [HIDDEN 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