{"id":3214076,"url":"https://alion.io/job/infobeans-technologies-data-scientist-generative-ai","title":"Data Scientist Generative AI","company":{"id":59407,"name":"InfoBeans Technologies","domain":"infobeans.ai","url":"https://alion.io/company/infobeans-technologies","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","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":19000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"FastAPI","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"A2A","optional":true},{"name":"API Gateway","optional":true},{"name":"AWS Fargate","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Databricks","optional":true},{"name":"DSPy","optional":true},{"name":"Embeddings","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Power Automate","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"RAG","optional":true},{"name":"ServiceNow","optional":true},{"name":"Snowflake","optional":true},{"name":"UiPath","optional":true}],"status":"live","first_seen_at":"2026-10-24T17:24:22Z","employer_posted_date":null,"last_verified_at":"2026-10-24T17:24:22Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"About InfoBeans\nAt InfoBeans, our philosophy is simple: we design our company to be our team's second home. We are an AI-first software engineering and digital transformation partner committed to making other people's lives better—through our work, our technology, and our everyday interactions.\nOur culture is caring, collaborative, experimental, and ethical. Guided by our core pillars of Excellence, Ownership, Compassion, and Openness, we strive to go beyond the ordinary. Creating WOW! is not just a tagline for us; it's our culture. If you love solving complex software engineering challenges while working in an environment where your voice is heard and your ideas are valued, you'll fit right in.\n\nRole Overview\nWe are looking for a highly skilled AI Engineer with a strong foundation in software engineering, Python development, and cloud-native application development. The role focuses on building, deploying, and integrating intelligent AI agents powered by Large Language Models (LLMs) into enterprise systems.\nThe ideal candidate will have hands-on experience developing scalable backend applications, REST APIs, and cloud-based solutions. Candidates with strong software engineering backgrounds and an interest in building production-ready AI applications are encouraged to apply.\n\nExperience: 6–10 years\n\nKey Responsibilities\nAI Application Development: Design, develop, and deploy scalable serverless and containerized AI applications using Python and cloud-native services.\nAI Agent Development: Build intelligent AI agents that use LLMs as reasoning engines and integrate seamlessly with existing production systems.\nAPI Development: Develop robust RESTful APIs using FastAPI and ensure seamless communication between applications and enterprise systems.\nSystem Integration: Collaborate with cross-functional teams to integrate AI solutions with internal platforms, enterprise applications, and third-party services.\nCloud-Native Engineering: Develop and maintain applications using microservices, serverless architectures, containers, and cloud services.\nPerformance Optimization: Analyze system performance, identify bottlenecks, and improve application scalability, latency, and reliability.\nMonitoring & Troubleshooting: Monitor AI applications and troubleshoot issues related to connectivity, timeouts, latency, and system integrations.\nAI Observability: Implement monitoring and evaluation practices to track the performance, reliability, and effectiveness of AI-powered systems.\nContinuous Improvement: Apply software engineering best practices, DevOps principles, and automated testing to maintain high-quality production applications.\n\nRequired Skills & Qualifications\nStrong understanding of software engineering principles, design patterns, and development best practices.\nProficiency in Python programming and backend application development.\nHands-on experience with FastAPI and RESTful API development.\nExperience working with cloud platforms such as AWS or Microsoft Azure.\nStrong understanding of cloud-native architecture, microservices, and serverless computing.\nKnowledge of containerization, deployment practices, and DevOps principles.\nBasic understanding of Large Language Models (LLMs), Generative AI, and MLOps.\nExperience with complex system integrations and connecting applications to enterprise platforms.\nUnderstanding of application monitoring, troubleshooting, latency, scalability, and reliability.\nStrong analytical, problem-solving, and debugging skills.\nAbility to collaborate effectively with cross-functional teams and stakeholders.\nGood communication skills and the ability to work in a collaborative engineering environment.\nKey Skill Areas\nPython & Backend Development: Building scalable backend services and reusable application components.\nFastAPI & REST APIs: Developing reliable APIs for AI applications and enterprise integrations.\nCloud Platforms: AWS or Azure services, cloud-native architecture, and serverless applications.\nAI Agents & LLMs: Integrating LLMs into applications and developing intelligent agent-based workflows.\nMicroservices & System Integration: Connecting AI solutions with enterprise systems and external services.\nPerformance Engineering: Troubleshooting latency, timeouts, connectivity, and scalability issues.\nDevOps & Monitoring: Application deployment, observability, automated testing, and production support.\nGood to Have\nExperience working with Databricks.\nExperience with AWS services such as Lambda, API Gateway, and Fargate.\nExperience integrating with enterprise platforms such as Snowflake, SAP, ServiceNow, or Salesforce.\nFamiliarity with LLM frameworks such as LangChain, LlamaIndex, or DSPy.\nKnowledge of prompt engineering and prompt optimization techniques.\nUnderstanding of AI agent communication protocols such as A2A and MCP.\nFamiliarity with agent evaluation frameworks and testing methodologies.\nExperience with vector databases, embeddings, and retrieval-augmented generation (RAG).\nAwareness of RPA tools such as UiPath or Microsoft Power 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