{"id":3924275,"url":"https://alion.io/job/deloitte-senior-backend-developer","title":"Senior Backend Developer","company":{"id":272,"name":"Deloitte","domain":"deloitte.com","url":"https://alion.io/company/deloitte","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":9,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Backend","role_family":"Backend","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":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"Falcon","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Mistral","optional":false},{"name":"Mixtral","optional":false},{"name":"Python","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-11-07T17:35:15Z","employer_posted_date":null,"last_verified_at":"2026-11-07T17:35:15Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Agentic App Engineer:\nLead the design and development of agentic and intelligent systems with strong ownership of core algorithms, model behavior, and decision logic, minimizing reliance on managed AI services and abstraction-heavy frameworks.\nArchitect end-to-end agentic systems with deep control over model internals, reasoning flows, tool-use logic, and execution pipelines rather than wrapper-based orchestration.\nTake algorithmic and model-level ownership of agent behavior, including reasoning strategies, planning algorithms, fallback logic, and failure handling.\nDesign and implement custom agent orchestration, memory systems, context management, and stateful workflows without over-dependence on LangChain/LangGraph-style abstractions.\nBuild and fine-tune open-source and self-hosted LLMs or hybrid approaches (open-weight + proprietary) based on performance, cost, and security trade-offs.\nDevelop custom RAG pipelines, embedding strategies, ranking, re-ranking, and grounding mechanisms tailored to enterprise knowledge systems.\nIntegrate agentic solutions with enterprise applications, APIs, data platforms, and internal ML systems.\nEstablish low-level design patterns, reusable libraries, and internal frameworks for building agent-based systems from first principles.\nOptimize model inference, reasoning efficiency, latency, cost, and response quality, including prompt-free and programmatic reasoning approaches where applicable.\nDrive Responsible AI practices with in-house guardrails, explainability techniques, auditability, and governance controls.\nCollaborate with business stakeholders to translate complex requirements into deterministic, reliable agent-driven solutions, avoiding black-box dependencies.\nLead production support, deep debugging, RCA, and performance tuning at both system and model levels.\nMentor junior consultants on core AI concepts, algorithms, and system design, not just framework usage.\n\nEducation:\n\nBE / B.Tech in Computer Science, Engineering, or a related discipline.\n\nRequired Experience & Skills.:\n4 to 7 years of strong experience building AI-driven systems, with demonstrated ownership of model behaviour and algorithms, not just API integration.\nDeep understanding of agentic architectures, multi-agent coordination, autonomous decision-making systems, and custom orchestration approaches.\nAbility to design agent frameworks from scratch or significantly extend existing ones rather than relying on out-of-the-box abstractions.\nStrong understanding of LLM internals, transformer architectures, inference behavior, tokenization, attention, and trade-offs.\nExperience with open-source LLMs (e.g., LLaMA-family, Mistral, Mixtral, Falcon, Qwen) and self-hosted deployments.\nAdvanced reasoning strategies: chain-of-thought alternatives, tree/graph-based reasoning, planners, critic-agent loops, and tool reasoning.\nPrompting as a controlled technique, not the primary abstraction.\nExperience with LangChain, LangGraph, AutoGen, CrewAI as reference tools, with proven ability to bypass, customize, or replace them with custom implementations.\nPreference for code-first, algorithm-driven approaches over declarative or black-box pipelines.\nDeep experience in RAG pipelines, embeddings, vector similarity search, hybrid retrieval, reranking models, and knowledge grounding.\nStrong grasp of information retrieval, indexing, and retrieval algorithms.\nStrong systems engineering skills with Python and/or Java/Go for AI backends.\nExperience deploying AI workloads on GCP, including self-managed inference, GPUs, containers, and scaling strategies.\nFamiliarity with MLOps practices: model versioning, evaluation pipelines, controlled rollouts, and experimentation.\nDeep experience in AI observability: tracing reasoning paths, latency profiling, quality evaluation, and cost monitoring.\nStrong understanding of AI security, data privacy, access control, and compliance, with preference for in-house controls over managed tooling.\nGeneral Insurance (Health, Motor, Travel) domain knowledge is a strong advantage.\n\nSoft Skills:\nDemonstrates strong ownership mindset, curiosity at the algorithmic level, and comfort working with ambiguity and complex systems.\nCollaborates effectively across engineering, data science, and business teams, clearly explaining technical trade-offs and design decisions.\nExhibits 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