{"id":1006172,"url":"https://alion.io/job/weekday-machine-learning-engineer-2","title":"Machine Learning Engineer - 2","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":100,"ghost_share":0,"stale_share":0.98,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":null,"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":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"NLP","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Weaviate","optional":false},{"name":"Hugging Face","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-09-17T16:36:51Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-11-09T21:04:40Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":53},"description":"'\n: - ( - )\nExperience: 3+ yrs\nLocation: Bengaluru\nJob Type: Full-time\nWe are looking for an experienced AI/ML Engineer to build and own production-grade Machine Learning and Generative AI systems end-to-end. The role focuses on developing intelligent applications using LLMs, RAG, conversational AI, agentic workflows, personalization, recommendations, memory, and user intelligence.\nThe ideal candidate will combine strong Python and software engineering fundamentals with hands-on experience building, evaluating, deploying, and optimizing AI systems for real-world applications. You will work across ML, retrieval, LLM orchestration, and scalable backend systems to deliver reliable and impactful AI-powered experiences.\nRequirements\nKey Responsibilities\nDesign, develop, and own production-grade ML/AI systems across the complete development lifecycle.\nBuild and integrate LLM-powered applications, including RAG pipelines, conversational AI, and agentic workflows.\nDevelop retrieval systems using embeddings, vector search, semantic retrieval, and context enrichment.\nBuild AI capabilities for personalization, memory, recommendations, and user intelligence.\nDesign LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic.\nDevelop evaluation frameworks to measure LLM quality, accuracy, relevance, reliability, latency, and cost.\nOptimize AI systems for production performance, scalability, response quality, and resource efficiency.\nCombine structured domain intelligence with ML, retrieval, and LLM reasoning to deliver context-aware outputs.\nBuild and maintain APIs and production services that integrate AI capabilities with backend systems.\nDesign scalable ML/AI architectures suitable for high-volume production environments.\nDevelop experiments, prototypes, and proof-of-concepts and transition successful solutions into production.\nImplement monitoring, evaluation, debugging, and continuous improvement processes for deployed AI systems.\nCollaborate with Product, Backend, and cross-functional engineering teams to deliver AI-powered features.\nEvaluate emerging LLMs, open-source models, retrieval techniques, agent frameworks, and AI tooling.\nContribute to engineering standards, technical documentation, model evaluation practices, and AI system design.\nTake ownership of problems end-to-end, from design and implementation through evaluation, deployment, and production support.\nWhat Makes You a Great Fit\n3+ years of experience in Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.\nStrong proficiency in Python with solid software engineering and programming fundamentals.\nHands-on experience building applications using LLMs, RAG, embeddings, vector search, or conversational AI.\nProven experience deploying and supporting ML/AI systems in production.\nStrong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization.\nExperience designing and developing AI APIs, scalable services, and production-ready systems.\nStrong understanding of system design, scalability, reliability, and cloud-based application development.\nExperience evaluating and optimizing LLM applications for quality, latency, cost, and reliability.\nStrong understanding of retrieval pipelines, prompt engineering, context management, and LLM orchestration.\nAbility to independently own technical problems across the complete lifecycle: design → build → evaluate → deploy → improve.\nExperience with LangChain or LangGraph is an advantage.\nFamiliarity with vector databases and technologies such as Pinecone, Weaviate, Milvus, pgvector, or similar is desirable.\nExperience with Hugging Face and open-source LLMs is a plus.\nKnowledge of MLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLP is an advantage.\nStrong analytical and problem-solving skills with a practical, experimentation-driven approach.\nExcellent communication and collaboration skills with the ability to work effectively across Product and Engineering teams.\nStrong ownership mindset and interest in building reliable, scalable, and user-focused AI 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