{"id":2802774,"url":"https://alion.io/job/novigo-solutions-artificial-intelligence-engineer","title":"Artificial Intelligence Engineer","company":{"id":20216,"name":"Novigo Solutions","domain":"novigosolutions.com","url":"https://alion.io/company/novigo-solutions","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":46000,"period":"year","method":"role_seniority_country_cell","sample_n":37},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"API Gateway","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"Gemini","optional":false},{"name":"Google AI Studio","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Mistral","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Weaviate","optional":false},{"name":"Apex","optional":true},{"name":"Databricks","optional":true},{"name":"Machine Learning","optional":true},{"name":"MuleSoft","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Power Apps","optional":true},{"name":"Power Automate","optional":true}],"status":"live","first_seen_at":"2026-07-28T10:36:25Z","employer_posted_date":null,"last_verified_at":"2026-07-28T10:36:25Z","board_verified":false,"closed_at":null,"days_open":82,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":82},"description":"Job Description : \n\nWe are seeking a highly skilled and experienced Senior AI Engineer / Technical Lead to join our growing AI & Digital Innovation team. \n\nThe successful candidate will play a central role in the design and development of enterprise-grade AI solutions, with a focus on building scalable, extensible platforms that support both conversational and transactional AI capabilities across multiple business functions. \n\nThis is a hands-on technical role suited to an engineer who combines deep AI implementation experience with the ability to lead technically, influence architecture decisions, and collaborate effectively with cross-functional teams. The ideal candidate is passionate about applied AI, has a strong engineering foundation, and has demonstrated experience delivering AI solutions in complex enterprise environments.\n\nKey Responsibilities : \n\nAI Solution Design & Development : \n\n- Design, develop, and maintain scalable AI solutions that support both conversational (chat-based) and transactional business functions.\n\n- Contribute to the architecture of AI platforms that are modular and extensible, enabling adoption across multiple business units with varying requirements.\n\n- Evaluate and recommend appropriate AI tools, models, frameworks, and platforms based on use case requirements and organisational standards.\n\n- Implement orchestration patterns that coordinate AI agents, models, and workflows in a coherent and reliable manner.\n\nAI Integration & Engineering : \n\n- Build and maintain integrations between AI components and enterprise systems including CRM, ERP, document management, HR platforms, and communication tools.\n\n- Design and implement APIs and integration patterns that enable seamless connectivity between AI capabilities and consuming applications.\n\n- Implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and multi-model coordination patterns based on solution requirements.\n\n- Ensure AI solutions are engineered for performance, reliability, and maintainability in production environments.\n\nConversational & Transactional AI : \n\n- Develop conversational AI capabilities including intelligent chatbots, virtual assistants, and LLM-powered dialogue systems.\n\n- Build transactional AI functions that automate or augment business processes such as document processing, data retrieval, approvals, and workflow automation.\n\n- Support the design of solutions that handle both real-time and batch AI processing depending on the nature of the business use case.\n\nGovernance, Security & Responsible AI : \n\n- Apply responsible AI principles throughout the development lifecycle including fairness, transparency, explainability, and data privacy.\n\n- Adhere to and contribute to AI governance practices covering model versioning, monitoring, and auditability.\n\n- Ensure AI solutions are built in compliance with organisational security policies, data handling standards, and relevant regulatory requirements.\n\n- Collaborate with the Information Security team to embed appropriate controls within AI solution design.\n\nTechnical Leadership & Collaboration : \n\n- Serve as a technical lead within the AI team, providing guidance and mentorship to junior engineers and developers.\n\n- Work closely with solution architects, business analysts, and product owners to translate business requirements into sound technical designs.\n\n- Participate in design reviews, technical discussions, and proof-of-concept evaluations.\n\n- Contribute to the development of internal standards, engineering guidelines, and reusable AI components.\n\n- Stay current with advancements in the AI space and proactively identify opportunities to apply emerging capabilities within the organisation.\n\nRequired Qualifications & Experience : \n\nEducation : \n\n- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical discipline.\n\nExperience : \n\n- Minimum 6-8 years of overall experience in software or AI engineering.\n\n- Minimum 3-4 years of hands-on experience designing and implementing production AI solutions in an enterprise environment.\n\n- Proven experience working on AI platforms or solutions that span conversational and process automation use cases.\n\n- Experience contributing to or leading the technical delivery of AI integration projects involving multiple enterprise systems.\n\nTechnical Skills : \n\n- AI & ML Frameworks : LangChain, Semantic Kernel, AutoGen, LlamaIndex, or equivalent AI orchestration frameworks.\n\n- Large Language Models : OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, or open-source LLMs (LLaMA, Mistral).\n\n- Vector Databases : Pinecone, Weaviate, Azure AI Search, pgvector, or equivalent.\n\n- Cloud (Azure Primary) : Azure OpenAI Service, Azure AI Studio, Azure Bot Services, Azure API Management, Azure Functions, Azure Logic Apps.\n\n- Azure Infrastructure : Azure Kubernetes Service (AKS), Azure Container Apps, Azure Service Bus, Azure Key Vault, Azure Monitor.\n\n- Programming Languages : Python (primary), with working proficiency in at least one of JavaScript, C#, or Java.\n\n- API & Integration : REST API design, event-driven architecture, webhook patterns, API gateway management.\n\n- RAG & Knowledge Retrieval : Retrieval-Augmented Generation (RAG), embedding models, semantic search, knowledge base integration.\n\n- Security & Governance : AI security controls, data privacy compliance, responsible AI principles.\n\n- DevOps & MLOps : CI/CD pipelines, containerisation (Docker, Kubernetes), model versioning, prompt management, and monitoring.\n\nPreferred Qualifications : \n\n- Hands-on experience with the Microsoft Azure AI and integration stack, including Azure AI Studio, Azure Integration Services, and Azure API Management.\n\n- Familiarity with agentic AI design patterns and multi-agent coordination frameworks.\n\n- Experience working in regulated industries such as aviation, finance, healthcare, or government.\n\n- Exposure to enterprise integration platforms such as MuleSoft, Azure Integration Services, or equivalent middleware.\n\n- Microsoft Azure certifications such as Azure AI Engineer Associate (AI-102) or Azure Developer Associate (AZ-204) are an advantage.\n\n- Familiarity with Power Platform (Power Automate, Power Apps) in the context of AI-assisted workflows.\n\nKey Competencies : \n\n- Technical Depth : Strong hands-on engineering capability with the ability to move from concept to working solution.\n\n- Problem Solving : Approaches complex and ambiguous technical challenges in a structured and pragmatic manner.\n\n- Communication : Able to explain technical concepts and decisions clearly to both technical peers and non-technical stakeholders.\n\n- Collaboration : Works well within cross-functional teams and across business units with differing priorities.\n\n- Innovation Mindset : Actively follows developments in the AI space and brings relevant ideas and approaches to the team.\n\n- Ownership : Takes responsibility for the quality and reliability of solutions developed and proactively addresses issues.\n\n- Mentorship : Committed to uplifting team capability through knowledge sharing and hands-on guidance.\n\nKey Skills : \n\n- Azure Cloud, Artificial Intelligence, Retrieval Augmented Generation, Machine Learning, Data Bricks, Open AI, Fast API.\nSkills\nArtificial Intelligence, Machine Learning, LLM, OpenAI, VectorDB, Python, RAG, Cloud, Azure, 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