{"id":3575287,"url":"https://alion.io/job/aisensy-artificial-intelligence-engineer","title":"Artificial Intelligence Engineer","company":{"id":13319,"name":"Aisensy","domain":"aisensy.com","url":"https://alion.io/company/aisensy","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":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI SDK","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Transformers","optional":false},{"name":"Vercel AI SDK","optional":false},{"name":"Weaviate","optional":false},{"name":"WhatsApp","optional":false},{"name":"AWS","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"JavaScript","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LoRA","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Node JS","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-31T17:34:44Z","employer_posted_date":null,"last_verified_at":"2026-10-31T17:34:44Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"About AiSensy\n\nAiSensy is a WhatsApp-based Marketing & Engagement platform helping businesses drive customer engagement, retention, and revenue growth through WhatsApp.\n250,000+ businesses enabled with WhatsApp Engagement & Marketing\n800+ crore WhatsApp messages exchanged annually through the AiSensy platform\nTrusted by leading brands including Adani, Delhi Transport Corporation, Yakult, Godrej, Aditya Birla Hindalco, Wipro, Asian Paints, India Today Group, Skullcandy, Vivo, PhysicsWallah, Cosco, and more\nBusinesses drive 25–80% of their revenue through WhatsApp using AiSensy\nMission-driven, high-growth startup backed by Marsshot.vc, Bluelotus.vc, and 50+ angel investors\n\nAbout the Role\nWe are looking for a Senior AI Engineer with 4+ years of experience to design, build, and scale production-grade AI systems powering the next generation of AiSensy's WhatsApp engagement platform.\nYou will work hands-on across LLMs, RAG, AI Agents, embeddings, vector search, AI evaluation, and AI-powered microservices. You will own AI solutions end-to-end—from problem definition and architecture to development, deployment, monitoring, and continuous optimization.\nThis role is ideal for someone who has moved beyond experimentation and has real-world experience taking AI/ML systems into production, with a strong focus on scalability, accuracy, latency, reliability, and business impact.\n\nWhat You'll Own\nAI/ML & LLM Engineering\nDesign, develop, and deploy production-grade AI/ML solutions for real-world business use cases.\nBuild and optimize LLM-powered applications, RAG pipelines, AI Agents, and intelligent automation workflows.\nEvaluate and select appropriate models, architectures, prompting strategies, and retrieval approaches for different use cases.\nWork with OpenAI, Google Generative AI, and other foundation model providers.\nBuild reusable AI components and services that can scale across multiple product use cases.\nRAG & Vector Search\nDesign production-grade Retrieval-Augmented Generation (RAG) systems.\nDevelop effective document ingestion, chunking, embedding, indexing, retrieval, and reranking pipelines.\nWork with vector databases such as Pinecone, Qdrant, or equivalent technologies.\nOptimize retrieval quality, relevance, latency, and cost.\nImplement hybrid search and other advanced retrieval techniques where appropriate.\nAI Agents & Automation\nDesign and build AI Agents capable of reasoning, tool usage, workflow execution, and context management.\nIntegrate AI systems with internal APIs, databases, and third-party services.\nBuild reliable guardrails around agentic workflows to ensure accuracy, safety, and predictable behavior.\nDevelop AI-powered automation for customer engagement and business workflows.\nBackend & AI Infrastructure\nBuild high-performance AI microservices using Python and FastAPI/Flask.\nDesign scalable APIs and services for AI-powered product features.\nWork with MongoDB and other data stores for structured and unstructured AI data.\nDesign asynchronous and scalable workflows for AI inference and data processing.\nImplement caching and optimization strategies using technologies such as Redis where required.\nAI Evaluation & Optimization\nEstablish evaluation frameworks for LLM and RAG applications.\nMeasure metrics such as accuracy, relevance, groundedness, hallucination rate, latency, and cost.\nDevelop strategies to reduce hallucinations and improve response quality.\nOptimize prompts, retrieval strategies, model selection, and inference workflows.\nContinuously evaluate new models and AI technologies for potential production use.\nPerformance & Reliability\nOptimize AI systems for latency, throughput, scalability, reliability, and cost.\nIdentify performance bottlenecks across model inference, retrieval, APIs, databases, and external integrations.\nDesign systems capable of handling high-volume production workloads.\nImplement appropriate logging, monitoring, error handling, and observability.\nEngineering & Technical Leadership\nOwn AI features from architecture and development through deployment and post-production optimization.\nParticipate in system design, architecture, and technical decision-making.\nConduct code and design reviews and maintain high engineering standards.\nMentor junior AI/ML engineers and contribute to best practices across the AI engineering team.\nCollaborate closely with Product Managers, Backend Engineers, Data teams, and leadership.\n\nMust-Have Qualifications\n4+ years of professional experience in AI/ML Engineering, Machine Learning Engineering, Applied AI, or a closely related role.\nStrong proficiency in Python and backend development.\nHands-on experience building and deploying production AI/ML systems.\nStrong practical experience with LLMs and Generative AI applications.\nHands-on experience building RAG pipelines, including chunking, embeddings, retrieval, and evaluation.\nExperience integrating OpenAI SDK, Google Generative AI SDK, or equivalent LLM APIs.\nStrong understanding of embeddings, semantic search, vector databases, and retrieval pipelines.\nExperience with FastAPI, Flask, or similar Python backend frameworks.\nExperience with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, or equivalent.\nExperience working with MongoDB or similar databases.\nStrong understanding of AI evaluation, hallucination mitigation, prompt engineering, and LLM optimization.\nGood understanding of ML fundamentals including Transformers, neural networks, classification, clustering, KNN, and model evaluation.\nUnderstanding of API security, authentication, data privacy, and secure AI application design.\nStrong debugging, system design, and problem-solving skills.\n\nGood to Have\nExperience with LangChain, LlamaIndex, LangGraph, or similar frameworks.\nHands-on experience building AI Agents / agentic workflows.\nExperience with multimodal AI applications.\nExposure to fine-tuning, LoRA/PEFT, or model adaptation workflows.\nExperience with hybrid search, reranking, BM25, or advanced retrieval optimization.\nUnderstanding of distributed systems and event-driven architectures.\nExperience with Redis or other caching systems.\nExperience optimizing LLM inference latency and cost.\nExperience with Docker, Kubernetes, AWS, or GCP.\nFamiliarity with CI/CD and production ML/AI deployment practices.\nExperience with Node.js for integrations or auxiliary services.\nExperience working on SaaS, MarTech, conversational AI, CRM, CPaaS, or customer engagement products.\n\nWhat We're Looking For\nSomeone who has built and shipped AI systems to production, not just worked on POCs.\nStrong engineering fundamentals combined with practical AI/ML expertise.\nAbility to translate ambiguous business problems into scalable AI solutions.\nStrong ownership of system quality, accuracy, performance, and reliability.\nComfortable making technical decisions independently.\nStrong experimentation mindset with a focus on measurable outcomes.\nAbility to mentor engineers and raise the technical bar of the team.\nComfortable working in a fast-paced, high-growth startup environment.\n\nWhat Success Looks Like\nProduction AI features are delivered reliably and adopted by users.\nRAG and AI Agent systems achieve strong accuracy and relevance.\nAI applications maintain low latency and high reliability at scale.\nHallucination and failure rates are continuously reduced.\nAI infrastructure is optimized for performance and cost.\nAI capabilities create measurable impact on customer experience and business outcomes.\nEngineering standards and AI development practices improve across the team.\n\nWhy Join AiSensy\nBuild AI products powering customer engagement for 250,000+ businesses.\nWork on real-world LLM, RAG, Agentic AI, and conversational AI problems at scale.\nOwn AI systems from architecture to production.\nWork closely with Product, Engineering, and senior leadership.\nJoin a high-growth, mission-driven SaaS company.\nOpportunity to shape AiSensy's next generation of AI-powered products.","description_format":"text","description_chars":7963,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Marketing & Sales AI","Messaging Apps"],"lifecycle":[{"event":"open","at":"2026-10-31T20:09:45Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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