{"id":1565739,"url":"https://alion.io/job/the-seo-byte-lead-ai-engineer","title":"Lead AI Engineer","company":{"id":3800305,"name":"The Seo Byte","domain":"theseobyte.in","url":"https://alion.io/company/the-seo-byte","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":"lead","employment_type":null,"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":24000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Helicone","optional":false},{"name":"HIPAA","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"Anthropic","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Claude","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Machine Learning","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Neo4j","optional":true},{"name":"OpenAI","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"RLHF","optional":true},{"name":"Vertex AI Agent Builder","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-01T05:52:08Z","employer_posted_date":null,"last_verified_at":"2026-10-01T05:52:08Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Roles & Responsibilities : \n\n- Design and implement complex components of agentic pipelines - multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems - using LangGraph, AutoGen, CrewAI, or equivalent.\n\n- Take ownership of full sub-system designs : define agent topology, data flows, API contracts, and failure handling for a bounded scope.\n\n- Build and optimise production RAG pipelines : document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.\n\n- Integrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.\n\n- Own observability for components : instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.\n\n- Lead CI/CD for owned modules : containerisation (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures.\n\n- Translate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications.\n\n- Apply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component.\n\n- Build intelligent document processing pipelines for pharma content : drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.\n\n- Contribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies.\n\n- Serve as the day-to-day technical reference for AI Engineers on the pod : code review, design feedback, unblocking implementation issues.\n\n- Lead component-level design reviews and surface architecture risks before they reach staging.\n\n- Pair with junior engineers on hard problems and document patterns and decisions in the team's shared knowledge base.\n\n- Represent engineering quality in client-facing technical discussions and translate complex trade-offs into plain language.\n\n- Contribute reference implementations and guardrail templates to the firm's internal agentic AI playbook.\n\nIdeal Candidate : \n\nProfile : \n\n- Strong Lead AI Engineer Profile with agentic systems architecture expertise and pharma regulated-environment experience.\n\nExperience : \n\n- Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems in pharma domain.\n\nTech Stack : \n\n- Frameworks : LangGraph, LangChain, AutoGen, CrewAI.\n\n- LLM APIs : Anthropic Claude, OpenAI Assistants API, Vertex AI Agent Builder.\n\n- Python : Production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.\n\n- RAG : Embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS.\n\n- Cloud/DevOps : AWS, Azure, or GCP; Docker, Kubernetes, Terraform/CDK, CI/CD pipelines.\n\n- Observability : LangSmith, Helicone.\n\nDomain Expertise : \n\n- Pharma commercial data (Rx/claims, NPI-level analytics, brand performance metrics).\n\n- Regulated environments (GxP, 21 CFR Part 11, HIPAA).\n\n- Medical affairs analytics, RWE, clinical operations, HEOR/market access, or regulatory intelligence.\n\nPreferred Skills : \n\n- MCP (Model Context Protocol); Veeva Vault, Medidata, IQVIA, or Symphony Health integrations; knowledge graphs (Neo4j, Amazon Neptune); RLHF/fine-tuning/model adaptation.\n\nAvailability : \n\n- Immediate joiner or currently serving notice period, able to start within the next week.\n\nSkills\nPython, LangChain, LangGraph, RAG, Machine Learning, Artificial Intelligence, Agentic AI","description_format":"text","description_chars":3612,"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":["Sales & Marketing","Search Engine Optimization (SEO)","Digital Marketing"],"lifecycle":[{"event":"open","at":"2026-10-01T06:00:00Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":24,"reasons":["seen:0","win:early"],"computed_at":"2026-10-02T02:42:10Z"},"pay":null,"html_url":"https://alion.io/job/the-seo-byte-lead-ai-engineer","json_url":"https://alion.io/job/the-seo-byte-lead-ai-engineer.json","meta":{"generated_at":"2026-10-02T02:42:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3655,"day_limit":5000,"remaining_today":1345,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}