{"id":1309328,"url":"https://alion.io/job/trintech-principal-aiml-engineer-lead","title":"Principal AI/ML Engineer Lead","company":{"id":2212182,"name":"Trintech","domain":"trintech.com","url":"https://alion.io/company/trintech","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-05T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","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":30},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Azure","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"Letta","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Mem0","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"Reranking","optional":false},{"name":"RLHF","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-07-27T00:00:00Z","employer_posted_date":"2026-07-27","last_verified_at":"2026-10-05T22:55:47Z","board_verified":true,"closed_at":null,"days_open":71,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":71},"description":"Description\nWHAT YOU’LL DO \nLLM Engineering Standards Across Agent PodsDefine and own LLM engineering standards across all Agent Stream pods - agent framework conventions, prompt lifecycle standards, eval harness design patterns, guardrail implementation, and confidence threshold calibration methodology that all Senior AI/ML Engineers follow.\nOwn the Langfuse observability framework at platform level - instrumentation standards, trace validation patterns, eval pipeline design, prompt regression testing, and model version regression detection. Pod-level Langfuse usage is consistent because this role defines how it is done.\nStandardise RAG pipeline architecture across pods - embedding strategy, vector database selection and management, retrieval strategy, reranking, and structured output design for financial document reasoning. Shared RAG infrastructure is your design.\nReview and challenge agent design proposals from pod AI/ML engineers - raise the bar on prompt design, memory architecture, evaluation rigour, and production reliability across the stream.\nContribute to AI/ML hiring - define the technical bar for AI/ML engineers across the stream, participate in interviews, and ensure hiring standards are consistent across pods.\n\nMemory Architecture OwnershipOwn the memory architecture strategy for the AI Platform - designing the personalised, complex memory layer that agents depend on for continuity, context, and adaptive behaviour across sessions and users.\nDesign and implement the tiered memory architecture for agent workflows - working memory (in-context), episodic memory (past interactions), semantic memory (extracted facts and preferences), and procedural memory (agent instruction updates). Select and implement the right memory framework for each tier: LangMem for LangGraph-native flows, Mem0 for managed personalisation, Zep/Graphiti for temporal and knowledge-graph reasoning, or Letta for explicit OS-style memory management.\nDefine memory hygiene standards - extraction policies, deduplication, contradiction resolution, and forgetting policies for agents that write aggressively to memory at scale.\nOwn context window management strategy - how long-running agent workflows handle context pressure, when to compress, when to retrieve from memory, and how to maintain coherence across multi-step financial close workflows.\n\nPlatform AI/ML ContributionContribute hands-on to Platform Team AI capabilities - working with the Platform Architect on how RAG, memory, and eval infrastructure is exposed as shared platform services that agent pods consume.\nStay current on the LLM and agent engineering landscape - evaluate new frameworks, protocols, and tooling (MCP, A2A, new memory systems, emerging eval approaches) and bring informed recommendations to the Director of Engineering on what to adopt and when.\nIdentify and address systemic AI/ML quality gaps across pods - inconsistent eval practices, weak guardrail implementations, or memory architectures that will not scale.\n\nWHO YOU ARE \nExtensive experience in AI/ML engineering, LLM engineering, data science, software engineering, or a related technical discipline, including experience delivering production-grade AI or ML solutions.\nStrong hands-on experience with production-grade LLM agent development, including LangChain, LangGraph, or similar agent frameworks.\nExperience defining platform-level engineering standards, architecture patterns, reusable frameworks, or technical practices across multiple teams or product areas.\nStrong understanding of prompt lifecycle management, including prompt versioning, rollback, environment-specific configuration, evaluation harnesses, and regression testing.\nExperience with LLM evaluation and observability practices, including confidence threshold calibration, guardrail design, model regression detection, trace validation, and tools such as Langfuse or similar platforms.\nExperience designing RAG pipeline architecture, including embedding strategies, vector database selection, retrieval strategies, hybrid search, reranking, and structured output design.\nExperience designing or implementing agent memory systems, context management strategies, or long-running agentic workflows.\nStrong data science foundation, including model evaluation, statistical reasoning, experimental design, and understanding of model behavior in non-deterministic or edge-case scenarios.\nProduction engineering experience with Python and related API frameworks, such as FastAPI or similar tools.\nExperience with PostgreSQL, pgvector or similar vector database technologies, Docker, Kubernetes, and Azure OpenAI or equivalent LLM providers.\nAbility to evaluate emerging AI/ML technologies, make informed architecture recommendations, and guide technical decisions across teams.\nStrong communication, collaboration, technical leadership, and problem-solving skills.\nExperience with Ragas or equivalent RAG evaluation frameworks preferred.\nExperience with model fine-tuning or RLHF preferred.\nExperience with financial close, Record-to-Report, accounting, or enterprise finance software preferred.\nAt our core, Trintechers stand committed to fostering a culture rooted in our core values - Humble, Empowered, Reliable, and Open. Together, these values guide our actions, define our identity, and inspire us to continuously strive for excellence in everything we do.\nAll qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin or disability.","description_format":"text","description_chars":5564,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Financial Services","Accounting & Tax Software"],"lifecycle":[{"event":"open","at":"2026-09-26T15:20:18Z"}],"visa":[],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":0.9,"p_active":0.577,"p_room":0.36,"age_days":70,"expected_fill_days":38,"reasons":["conf:62","velocity","win:tail","crowd:"],"computed_at":"2026-10-05T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/trintech-principal-aiml-engineer-lead","json_url":"https://alion.io/job/trintech-principal-aiml-engineer-lead.json","meta":{"generated_at":"2026-10-06T02:18:26Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3421,"day_limit":5000,"remaining_today":1579,"minute_limit":60,"resets_at":"2026-10-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2212182},"rest":"https://alion.io/mcp/rest/get_company?id=2212182"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftrintech-principal-aiml-engineer-lead"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftrintech-principal-aiml-engineer-lead"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftrintech-principal-aiml-engineer-lead"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/trintech-principal-aiml-engineer-lead\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftrintech-principal-aiml-engineer-lead"}]}