Job
Description
About The Role
Project Role :Software Development Lead
Project Role Description :Develop and configure software systems either end-to-end or for a specific stage of product lifecycle. Apply knowledge of technologies, applications, methodologies, processes and tools to support a client, project or entity.
Must have skills :Machine Learning
Good to have skills :Python (Programming Language), Agile Project Management
Minimum 12 year(s) of experience is required
Educational Qualification :BTECH
SummaryLead ML EngineerLead the design and delivery of AI solutions across Agentic AI, Generative AI (LLMs) and classical ML/CV. Own the technical direction for suggestion & rules frameworks, search/retrieval, document and web data extraction, and image/OCR pipelines for the Value Stream. Provide architectural leadership, mentor engineers, and ensure production-grade quality, safety, and reliability. Should be familiar with evaluation strategies, responsible AI, explainability. Roles and responsibilities:
Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components.Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs.Architect search & retrieval (BM25 + embeddings) and RAG pipelines; drive relevance tuning and evaluation.Oversee robust scraping & extraction (Playwright/Selenium/Trafilatura) and structured normalization (JSON/Parquet, schema validation).Direct image processing and OCR workflows (OpenCV, pytesseract/ocrmypdf) for document understanding.Establish evaluation strategy:offline/online experiments, quality/latency/cost KPIs; integrate DeepEval for unit-style LLM tests.Guide data governance, privacy/PII handling, and secure model/agent operations with MLOps partners.Mentor the team, run design reviews, and produce clear design docs, RFCs, and POVs for stakeholders.Technical experience & Professional attributes:Model generalization vs. overfitting/underfitting; bias/variance trade-offs; regularization and early stopping.Deep learning fundamentals:CNNs, RNNs/LSTMs/GRUs, and modern transformers; encoder/decoder architectures and attention.LLM inner-workings at a practical level:tokenization, context windows, inference strategies (batching, caching, quantization), fine-tuning/PEFT, and RAG.Inference and serving techniques for throughput/cost (vectorization, mixed precision, compile/acceleration paths where applicable).Tooling FamiliarityPyTorch; Hugging Face ecosystem (transformers, datasets, sentence-transformers/SBERT); BERT/Llama families as applicable.LangChain for orchestration; familiarity with LangGraph/LangMem for agentic workflows (subject to approval).spaCy, scikit-learn; LightGBM/Flair where relevant; Optuna for HPO; SHAP for model explainability.Search:Elastic/OpenSearch; vector stores (FAISS/Pinecone/pgvector); docarray for embedding flows.Document & web data:Playwright/Selenium, Trafilatura, pypdf, pdfplumber, pdfkit; tokenization tools like tiktoken.Stakeholder demos:Streamlit (local-only).Education qualifications:Proven record architecting and shipping production ML/LLM systems.Strong written and verbal communication; experience leading Agile delivery and cross-functional collaboration.You will be working with a Trusted Tax Technology Leader, committed to delivering reliable and innovative solutions
Qualification BTECH