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.
Responsibilities
- Own and deliver end-to-end backend systems for AI product runtime, including orchestration, request lifecycle management, state/session handling, and policy enforcement.
- Design 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.
- Productionize ML workflows and interfaces, including feature and metadata services, online/offline parity, model integration contracts, and evaluation instrumentation.
- Drive performance, reliability, and cost optimization, owning P50/P95 latency, throughput, cache hit rates, token and inference costs, and infrastructure efficiency.
- Build observability by default, including structured logs, metrics, distributed tracing, guardrail signals, failure taxonomies, and reliable fallback paths.
- Collaborate closely with Applied ML teams on model routing, prompt and tool schemas, evaluation datasets, and release safety gates.
- Write clean, testable, and maintainable backend code, contributing to design reviews, code reviews, and operational best practices.
- Take systems from design → build → deploy → operate, including on-call ownership and incident response.
- Continuously identify bottlenecks and failure modes in AI-backed systems and proactively improve system robustness.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 6–10 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.
- Strong practical understanding of ML system fundamentals, including embeddings, vector similarity, reranking, retrieval quality, and evaluation metrics (precision/recall, nDCG, MRR).
- Proven experience implementing or operating RAG pipelines, covering ingestion, chunking, indexing, query understanding, hybrid retrieval, and rerankers.
- Solid distributed systems fundamentals, including API design, idempotency, concurrency, retries, circuit breakers, rate limiting, and multi-tenant reliability.
- Experience with common ML/AI platform components, such as feature stores, metadata systems, streaming or batch pipelines, offline evaluation jobs, and A/B measurement hooks.
- Strong proficiency in backend programming languages and frameworks and API development.
- Has built AI applications end-to-end, not just pipelines (RAG systems, agentic workflows, chatbots, internal AI tools).
- Deep understanding of retrieval including chunking strategies, Embeddings, indexing & re-ranking, retrieval failures & tuning
- Has owned full RAG pipelines: ingestion → chunking → indexing → retrieval → generation
- Experience with agentic frameworks (tool calling, Memory, multi-step reasoning)
- Thinks in terms of intelligence delivery
- Production mindset ( evals, guardrails, latency / cost trade-offs)
- Clear understanding of Managed AI Services (fallback mechanisms, service reliability & failure handling)
- Clear understanding of MCPs (Model Control / Management Plans)
- Experience with agentic runtimes, including tool-calling or function-calling patterns, structured outputs, and production guardrails.
- Hands-on exposure to vector and hybrid retrieval stacks such as FAISS, Milvus, Pinecone, or Elasticsearch.
- Experience running systems on Kubernetes, with strong knowledge of observability stacks like OpenTelemetry, Prometheus, Grafana, and distributed tracing.
- Familiarity with privacy, security, and data governance considerations for user and model data.
- Expected exposure of Python, LangChain / LangGraph, Vector DBs, LLM APIs, GCP tooling (plus, not mandatory)
- Candidate must
- explain retrieval internals clearly
- have built something real using RAG / agents (internal or external)
- have worked on the application side of ML, not only infra or data plumbing
Benefits
- Best in class compensation: We hire only the best, and we pay accordingly.
- Proximity Talks: Learn from experienced engineers, designers, and product leaders across the organization.
- Work with a world-class team: Build scalable mobile products, push technical boundaries, and learn something new every day.
We are Proximity — a global team of coders, designers, product managers, geeks, and experts. We solve complex problems and build cutting-edge technology at scale.
Our team of Proxonauts is growing quickly, which means your impact on the company’s success will be significant. You’ll work with experienced leaders who have built and led high-performing tech, product, and design teams.
Here’s a quick guide to getting to know us better:
- Watch our CEO, Hardik Jagda, tell you all about Proximity.
- Read about Proximity’s values and meet some of our Proxonauts.
- Explore our website, blog, and design wing — Studio Proximity.
- Get behind the scenes with us on Instagram — follow @ProxWrks and @H.Jagda.
Top Skills
What We Do
We are Proximity — a global team of coders, designers, product managers, geeks and experts. We solve hard, long-term engineering problems and build cutting edge tech products
