Software Development Lead

4 - 7 years

9 - 13 Lacs

Posted:23 hours ago| Platform: Naukri logo

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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 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
     Qualification BTECH
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