AI / ML Engineer en Accenture
Accenture · Hyderabad, Indien · Onsite
- Senior
- Oficina en Hyderabad
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning
Good to have skills : Microsoft Azure Machine Learning
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
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 solutions15 years full time education
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning
Good to have skills : Microsoft Azure Machine Learning
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
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 solutions15 years full time education
About Accenture
We work with one shared purpose: to deliver on the promise of technology and human ingenuity. Every day, more than 775,000 of us help our stakeholders continuously reinvent. Together, we drive positive change and deliver value to our clients, partners, shareholders, communities, and each other.We believe that delivering value requires innovation, and innovation thrives in an inclusive and diverse environment. We actively foster a workplace free from bias, where everyone feels a sense of belonging and is respected and empowered to do their best work.At Accenture, we see well-being holistically, supporting our people’s physical, mental, and financial health. We also provide opportunities to keep skills relevant through certifications, learning, and diverse work experiences. We’re proud to be consistently recognized as one of the World’s Best Workplaces™.Join Accenture to work at the heart of change.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
