Custom Software Engineer

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Machine Learning
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary 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. 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. Concepts & Technical Awareness (Expected) 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). Professional & Technical Skills: 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). 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 Proven experience in cloud, DevOps, or AI modernization initiatives. Strong understanding of LLMs, automation, and engineering toolchains. Ability to translate AI innovation into business and productivity impact. Additional Information: - The candidate should have minimum 7.5 years of experience in Machine Learning. - This position is based at our Bengaluru office. - A 15 years full time education is required.