{"id":3267055,"url":"https://alion.io/job/proximity-backend-engineer-ai-ml","title":"Backend Engineer - AI / ML","company":{"id":6179,"name":"Proximity","domain":"proximity.tech","url":"https://alion.io/company/proximity-labs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":6,"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":"Feature Store","optional":false},{"name":"Function Calling","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"ElasticSearch","optional":true},{"name":"FAISS","optional":true},{"name":"GCP","optional":true},{"name":"Grafana","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM","optional":true},{"name":"Milvus","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Pinecone","optional":true},{"name":"Prometheus","optional":true},{"name":"Python","optional":true},{"name":"Structured Outputs","optional":true}],"status":"live","first_seen_at":"2026-10-26T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T09:35:00Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We are looking for a senior backend engineer to build and operate the core AI/ML-backed systems that power large-scale, consumer-facing products. You will work on production-grade AI runtimes, retrieval systems, and ML-adjacent backend infrastructure, making pragmatic tradeoffs across quality, latency, reliability, and cost. We are hiring applied AI engineers, not engineering-for-ML profiles. This role is not an entry point into AI/ML. You are expected to already have hands-on experience shipping ML-backed backend systems in production. At Proximity, you won't just build APIs; you'll own critical backend systems end-to-end, collaborate closely with Applied ML and Product teams, and help define the foundations that power intelligent experiences at scale.\n\nResponsibilities: \nOwn and deliver end-to-end backend systems for AI product runtime, including orchestration, request lifecycle management, state/session handling, and policy enforcement.\nDesign and implement retrieval and memory primitives, end-to-end document ingestion, chunking strategies, embeddings generation, indexing, vector/hybrid search, re-ranking, caching, freshness, and deletion semantics.\nProductionize ML workflows and interfaces, including feature and metadata services, online/offline parity, model integration contracts, and evaluation instrumentation.\nDrive performance, reliability, and cost optimization, owning P50/P95 latency, throughput, cache hit rates, token and inference costs, and infrastructure efficiency.\nBuild observability by default, including structured logs, metrics, distributed tracing, guardrail signals, failure taxonomies, and reliable fallback paths.\nCollaborate closely with Applied ML teams on model routing, prompt and tool schemas, evaluation datasets, and release safety gates.\nWrite clean, testable, and maintainable backend code, contributing to design reviews, code reviews, and operational best practices.\nTake systems from design, build, deploy, and operate, including on-call ownership and incident response.\nContinuously identify bottlenecks and failure modes in AI-backed systems and proactively improve system robustness.\n\nRequirements: \nBachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.\n6-10 years of experience building backend systems in production, with 2-3+ years working on ML/AI-backed products such as search, recommendations, ranking, RAG pipelines, or AI assistants.\nStrong practical understanding of ML system fundamentals, including embeddings, vector similarity, reranking, retrieval quality, and evaluation metrics (precision/recall, nDCG, MRR).\nProven experience implementing or operating RAG pipelines, covering ingestion, chunking, indexing, query understanding, hybrid retrieval, and rerankers.\nSolid distributed systems fundamentals, including API design, idempotency, concurrency, retries, circuit breakers, rate limiting, and multi-tenant reliability.\nExperience with common ML/AI platform components, such as feature stores, metadata systems, streaming or batch pipelines, offline evaluation jobs, and A/B measurement hooks.\nStrong proficiency in backend programming languages and frameworks and API development.\n\nWhat matters (non-negotiable): \nHas built AI applications end-to-end, not just pipelines (RAG systems, agentic workflows, chatbots, and internal AI tools).\nDeep understanding of retrieval, including chunking strategies, embeddings, indexing and re-ranking, retrieval failures, and tuning.\nHas owned full RAG pipelines: ingestion, chunking, indexing, retrieval, and generation.\nExperience with agentic frameworks (tool calling, memory, and multi-step reasoning).\nThinks in terms of intelligence delivery.\nProduction mindset (evals, guardrails, latency/cost trade-offs).\nClear understanding of managed AI services (fallback mechanisms, service reliability, and failure handling).\nClear understanding of MCPs (Model Control/Management Plans).\n\nDesired Skills: \nExperience with agentic runtimes, including tool-calling or function-calling patterns, structured outputs, and production guardrails.\nHands-on exposure to vector and hybrid retrieval stacks such as FAISS, Milvus, Pinecone, or Elasticsearch.\nExperience running systems on Kubernetes, with strong knowledge of observability stacks like OpenTelemetry, Prometheus, Grafana, and distributed tracing.\nFamiliarity with privacy, security, and data governance considerations for user and model data.\nExpected exposure to Python, LangChain / LangGraph, Vector DBs, LLM APIs, and GCP tooling (plus, not mandatory).\nThe candidate must explain retrieval internals clearly, have built something real using RAG/agents (internal or external), and have worked on the application side of ML, not only infra or data plumbing.\nSkills\nAI, ML, RAG, backend, embeddings, indexing, reranking, retrieval","description_format":"text","description_chars":4855,"description_truncated":false,"requirements":{"experience_years_min":6,"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":["Artificial Intelligence","Machine Learning","Custom Software Development","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-10-26T09:35:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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