Job Description

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 o Define end-to-end architecture for LLM/agent systems (tool use, orchestration, guardrails) and classical ML components. o Design suggestion engines and policy/rule layers that combine deterministic constraints with generative outputs. o Architect search & retrieval (BM25 + embeddings) and RAG pipelines; drive relevance tuning and evaluation. o Oversee robust scraping & extraction (Playwright/Selenium/Trafilatura) and structured normalization (JSON/Parquet, schema validation). o Direct image processing and OCR workflows (OpenCV, pytesseract/ocrmypdf) for document understanding. o Establish evaluation strategy: offline/online experiments, quality/latency/cost KPIs; integrate DeepEval for unit-style LLM tests. o Guide data governance, privacy/PII handling, and secure model/agent operations with MLOps partners. o Mentor the team, run design reviews, and produce clear design docs, RFCs, and POVs for stakeholders. Concepts & Technical Awareness (Expected) o Model generalization vs. overfitting/underfitting; bias/variance trade-offs; regularization and early stopping. o Deep learning fundamentals: CNNs, RNNs/LSTMs/GRUs, and modern transformers; encoder/decoder architectures and attention. o LLM inner-workings at a practical level: tokenization, context windows, inference strategies (batching, caching, quantization), fine-tuning/PEFT, and RAG. o Inference and serving techniques for throughput/cost (vectorization, mixed precision, compile/acceleration paths where applicable). Professional & Technical Skills: o PyTorch; Hugging Face ecosystem (transformers, datasets, sentence-transformers/SBERT); BERT/Llama families as applicable. o LangChain for orchestration; familiarity with LangGraph/LangMem for agentic workflows (subject to approval). o spaCy, scikit-learn; LightGBM/Flair where relevant; Optuna for HPO; SHAP for model explainability. o Search: Elastic/OpenSearch; vector stores (FAISS/Pinecone/pgvector); docarray for embedding flows. o Document & web data: Playwright/Selenium, Trafilatura, pypdf, pdfplumber, pdfkit; tokenization tools like tiktoken. o Stakeholder demos: Streamlit (local-only). Qualifications o Proven record architecting and shipping production ML/LLM systems. o Strong written and verbal communication; experience leading Agile delivery and cross-functional collaboration. o You will be working with a Trusted Tax Technology Leader, committed to delivering reliable and innovative solutions o Proven experience in cloud, DevOps, or AI modernization initiatives. o Strong understanding of LLMs, automation, and engineering toolchains. o 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.




15 years full time education