Talentgigs
Website:
talentgigs.in
Job details:
Senior AI/ML Developer Location:
Coimbatore Experience: 4+ Years
Notice: 30 Days
The Mission: Building the Intelligence Layer for Financial Operations Anaiyan is building intelligent systems that transform complex, fragmented financial and operational data into reliable, structured and actionable information. We are working at the intersection of AI/ML, financial data, accounting, automation and agentic systems—solving problems where traditional rules-based software struggles with ambiguity, variability and context. Our systems need to understand real-world data from sources such as POS systems, bank statements, CRM/ERP exports, spreadsheets, invoices and accounting systems; identify relationships between records; infer transformations; reconcile financial events; and progressively learn from human decisions. We are looking for an engineer who wants to solve difficult problems in machine learning, LLMs, document/data understanding, reasoning, evaluation and production AI systems. If you enjoy taking an ambiguous problem, determining where AI actually adds value, building the system, measuring its behaviour and making it reliable enough for production, this is the environment you’ve been looking for.
Core Responsibilities
AI/ML System Development Design and develop production-grade AI/ML systems for financial and operational workflows. Work across: • LLM-based systems • Machine learning and deep learning • Classification and prediction • Semantic search & Anomaly detection • Recommendation and ranking • Structured reasoning Build hybrid systems combining deterministic rules, statistical models, embeddings and LLMs where appropriate. Agentic AI Systems Design and implement controlled AI agents for complex financial workflows. Build reliable agent architectures with: • Tool calling • State management • Planning • Context management • Verification • Confidence assessment • Human-in-the-loop controls • Retry and recovery mechanisms • Auditability LLM Engineering Work with leading foundation models and develop production applications using: • Structured generation • Function/tool calling • RAG • Embeddings • Prompt/context engineering • Model evaluation & routing • Fine-tuning • Cost and latency optimization Evaluate when a problem should be solved using an LLM versus conventional ML, deterministic logic or a hybrid approach. AI Evaluation & Reliability Build rigorous evaluation frameworks for AI systems. Define and measure: • Precision/recall • Classification accuracy • False-positive/false-negative rates • Confidence calibration • LLM response quality • Agent task completion • Regression performance Build representative synthetic and real-world evaluation datasets and automated regression suites.
Technical & Professional Qualifications
AI/ML Engineering Experience — 4+ years in ML engineering & AI engineering, with significant hands-on experience building and deploying production AI/ML systems. LLM & Generative AI Expertise — strong hands-on experience with modern LLM application development, including several of: OpenAI APIs, Anthropic APIs, Hugging Face, LangChain/LangGraph, Pydantic AI, agent frameworks, structured outputs, tool/function calling, RAG, embeddings and LLM evaluation. Machine Learning Depth — strong understanding of supervised and unsupervised learning, classification, ranking, clustering, similarity/matching, NLP, information extraction, deep learning, model evaluation and statistical reasoning. Hands-on experience with PyTorch, scikit-learn or equivalent ML frameworks. Data & Backend Engineering — strong Python development skills and practical experience with SQL, PostgreSQL or equivalent databases, REST APIs, data pipelines, model/version management, Pandas/NumPy, JSON/CSV/Excel processing, Docker, Git and automated testing. Experience with distributed systems and event-driven architectures is valuable. AI/ML Production Engineering — experience deploying and operating AI/ML systems in production, with knowledge of model serving, MLOps/LLMOps, model/version management, observability, inference optimization, GPU/compute optimization, CI/CD, containerization and cloud infrastructure. Analytical Thinking — strong ability to break ambiguous problems into measurable components and determine the appropriate combination of rules, ML, LLMs, agents and human review, rather than defaulting to a single technology.
Who Should Apply This role is particularly suited for engineers who:
• Build systems and enjoy working with messy real-world data. • Are comfortable combining conventional engineering with AI. • Care deeply about evaluation and correctness. • Can reason about AI failure modes. • Are excited by agentic systems but understand their limitations. • Want to work on difficult financial-data problems. • Prefer solving open-ended problems over implementing predetermined specifications.
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