{"id":3084931,"url":"https://alion.io/job/citi-senior-lead-python-aiml-engineer","title":"Senior Lead Python AI/ML Engineer","company":{"id":7841,"name":"Citi","domain":"citi.com","url":"https://alion.io/company/citi","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":448,"ghost_share":0.007,"stale_share":0.098,"repost_share":0.083,"time_to_fill_p50_days":10,"computed_at":"2026-10-23T05:54:15Z"}},"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":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":58000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Asyncio","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Explainable AI","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LightGBM","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"RabbitMQ","optional":false},{"name":"RAG","optional":false},{"name":"Ray","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"TensorFlow","optional":false},{"name":"Weights & Biases","optional":false},{"name":"XGBoost","optional":false},{"name":"DSPy","optional":true},{"name":"DuckDB","optional":true},{"name":"ElasticSearch","optional":true},{"name":"FAISS","optional":true},{"name":"Function Calling","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Grafana","optional":true},{"name":"gRPC","optional":true},{"name":"Hugging Face","optional":true},{"name":"Kubeflow","optional":true},{"name":"LIME","optional":true},{"name":"OpenAI","optional":true},{"name":"Pandas","optional":true},{"name":"PEFT","optional":true},{"name":"Polars","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prometheus","optional":true},{"name":"Rest API","optional":true},{"name":"SHAP","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true},{"name":"Transformers","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-13T00:00:00Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-23T05:28:10Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"Role Overview\nWe are seeking an experienced and visionary Senior Lead Python AI/ML Engineer with Generative AI Capabilities to lead the design, development, and deployment of next-generation artificial intelligence and machine learning solutions based out of our Chennai center (operating in a Hybrid work model). With 10 to 12 years of hands-on software and machine learning engineering experience, the ideal candidate will drive the convergence of traditional predictive ML models and modern Foundation Model / GenAI architectures.\nIn this role, you will be instrumental in building high-scale, low-latency AI solutions applied to Fraud Mitigation, Risk Scoring, and Loss Prevention across US Retail Banking products\n2. Key Responsibilities\nAI/ML & Generative AI Architecture\nArchitect, build, and deploy production-grade Generative AI systems, including Retrieval-Augmented Generation (RAG), autonomous agents, semantic search pipelines, and multi-modal AI workflows.\nIntegrate state-of-the-art Large Language Models (LLMs)-including Google Gemini, Anthropic Claude, and open-source models (Llama, Mistral)-optimizing for latency, cost, and accuracy.\nBuild domain-specific prompt engineering frameworks, fine-tuning pipelines (LoRA/QLoRA), model evaluation benchmarks, and guardrails to minimize hallucinations and bias.\nLead the architecture of predictive ML algorithms (anomaly detection, graph neural networks, supervised/unsupervised classifiers) tailored to real-time risk assessment.\nSoftware Engineering & System Scalability\nDesign resilient, low-latency, and horizontally scalable microservices and APIs in Python (FastAPI, Flask, AsyncIO) handling high-throughput transactional traffic.\nIntegrate vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma, pgvector) with distributed caching and event streaming platforms (Kafka, RabbitMQ).\nImplement robust MLOps and LLMOps practices (CI/CD pipelines, automated model retraining, observability with MLflow/Weights & Biases, model drift monitoring).\nFraud Analytics & Domain Innovation\nCollaborate closely with US-based and global Risk, Compliance, and Loss Prevention teams to build AI solutions targeting identity theft, account takeover (ATO), synthetic identity fraud, card-not-present (CNP) fraud, and transaction anomaly detection.\nUtilize unstructured data (investigator notes, SAR filings, customer interaction logs) via GenAI to automate fraud triage and investigative summarization.\nEnsure strict adherence to US Banking regulatory standards (FCRA, GLBA, Model Risk Management SR 11-7, Fair Lending, Explainable AI / XAI).\nTechnical Leadership & Mentorship\nMentor senior/mid-level data scientists and software engineers across the Chennai engineering hub in modern GenAI patterns, clean code principles, and production readiness.\nDrive technical roadmap, evaluate emerging AI frameworks, conduct proof-of-concepts (PoCs), and present architectural proposals to global engineering leadership.\n3. Required Qualifications & Experience\nEducation: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.\nTotal Experience: 10 to 12 years of professional experience in software engineering and production AI/ML system development.\n Core Python Expertise: Advanced proficiency in Python 3.x (multiprocessing/asyncio, design patterns, profiling, packaging, and clean code paradigms).\nCore ML Frameworks: Deep practical experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM.\n Generative AI Proficiency:Demonstrated success building production GenAI applications (LangChain, LlamaIndex, CrewAI, AutoGen).\nHands-on experience with embedding models, dense retrieval, rerankers, and vector indexing strategies.\n\n Distributed Systems & Cloud: Strong hands-on experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and distributed data processing (PySpark, Ray).\nMLOps / LLMOps: Proven track record in orchestrating end-to-end ML lifecycles from data validation to real-time inference monitoring.\n4. Preferred & Good-to-Have Skills\n Google Gemini & Anthropic Claude: Hands-on experience with API integration, function calling/tools, context caching, and prompt optimization specifically for Google Gemini (Pro/Flash) and Anthropic Claude (Opus/Sonnet/Haiku).\n Domain Knowledge (Fraud & Loss Prevention):Experience designing fraud rule engines, anomaly detection platforms, or predictive risk models.\nFamiliarity with transaction monitoring, biometric behavioral analytics, and fraud consortium datasets.\n\n5. Technical Skills Matrix\nCategory\nTechnologies & Tools\nProgramming Languages\nPython (Advanced), SQL, Bash\nGenerative AI & LLMs\nAnthropic Claude, Google Gemini, OpenAI GPT, Open-source LLMs (Llama, Mistral), LangChain, LlamaIndex, DSPy\nClassical & Deep ML\nPyTorch, TensorFlow, Scikit-Learn, XGBoost, LightGBM, Hugging Face Transformers\nVector DBs & Search\nPinecone, Milvus, Qdrant, Weaviate, FAISS, pgvector, Elasticsearch\nBackend & APIs\nFastAPI, Flask, AsyncIO, gRPC, REST\nData & Streaming\nApache Kafka, Apache Spark / PySpark, Pandas, Polars, DuckDB\nMLOps & DevOps\nMLflow, Kubeflow, Docker, Kubernetes, GitHub Actions, Terraform, Prometheus/Grafana\nDomain Tools & Standards\nModel Risk Management (SR 11-7), Explainable AI (SHAP, LIME), PII Masking\n6. Key Performance Indicators (KPIs)\nModel Performance & Accuracy: Precision/Recall trade-offs in fraud detection, false positive reduction, and sub-100ms real-time inference latency.\n Engineering Excellence: Code quality, test coverage (>85%), modular design, and robust error handling.\nBusiness Impact: Measurable reduction in fraud loss basis points and acceleration of investigator workflow efficiency through GenAI summarization/triage.\nMentorship & Global Delivery: On-time milestone delivery, technical capability elevation across local and global engineering pods.\n------------------------------------------------------\nJob Family Group:\nTechnology------------------------------------------------------\nJob Family:\nApplications Development------------------------------------------------------\nTime Type:\nFull time------------------------------------------------------\nMost Relevant Skills\nPlease see the requirements listed above.------------------------------------------------------\nOther Relevant Skills\nFor complementary skills, please see above and/or contact the recruiter.------------------------------------------------------\nCiti is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.\nIf you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.\nView Citi’s EEO Policy Statement and the Know Your Rights poster.","description_format":"text","description_chars":7015,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Cards & Card Issuing","Wealth Management & Financial Advisors","Payment Processing & Gateways","Investment Banking & M&A Advisory"],"lifecycle":[{"event":"open","at":"2026-10-22T13:22:50Z"}],"visa":[],"liveness":{"score":43,"band":"fade","label":"Fading","p_open":1,"p_active":0.776,"p_room":0.55,"age_days":10,"expected_fill_days":10,"reasons":["conf:0","velocity","win:tail","comp:brand"],"computed_at":"2026-10-23T05:54:15Z"},"pay":null,"html_url":"https://alion.io/job/citi-senior-lead-python-aiml-engineer","json_url":"https://alion.io/job/citi-senior-lead-python-aiml-engineer.json","meta":{"generated_at":"2026-10-23T06:32:59Z","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_verified","counted_by":"address","units_charged":1,"used_today":10660,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-24T00:00:00Z"}},"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":7841},"rest":"https://alion.io/mcp/rest/get_company?id=7841"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fciti-senior-lead-python-aiml-engineer"},{"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%2Fciti-senior-lead-python-aiml-engineer"},{"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%2Fciti-senior-lead-python-aiml-engineer"},{"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/citi-senior-lead-python-aiml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fciti-senior-lead-python-aiml-engineer"}]}