Software Development Lead

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
Summary Lead ML Engineer Lead 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 Familiarity • PyTorch; 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