remoteonsite
Lead AI Consultant - NTT DATA
Software Engineer
We're looking for a Software Engineer focused on designing and building scalable technical solutions. This lead role requires 9+ years of relevant experience.
About the role
- 10+ years of overall experience in Software Engineering and Technology Solutions.
- 5+ years of hands-on experience in Machine Learning, Artificial Intelligence, and Natural Language Processing (NLP).
- 2+ years of experience designing and developing LLM-powered applications and Generative AI solutions.
- 1.5+ years of experience building and deploying Multi-Agent AI Systems in production environments.
- Proven track record of delivering scalable AI/ML solutions on cloud platforms.
- Lead the end-to-end development and deployment of Machine Learning and NLP solutions, from problem definition through productionisation.
- Design and implement feature engineering, model training, validation, evaluation, and monitoring pipelines.
- Establish best practices for model lifecycle management, explainability, fairness, and governance.
- Drive continuous improvement of model performance, scalability, and operational efficiency.
- Collaborate with business stakeholders to identify AI-driven opportunities and translate them into practical solutions.
- Design, develop, and deploy enterprise-grade applications powered by Large Language Models (LLMs).
- Build solutions leveraging models such as GPT, Llama, Gemini, Claude, and other leading foundation models.
- Develop and optimize prompt engineering strategies, prompt orchestration, evaluation frameworks, and fine-tuning workflows.
- Integrate LLM capabilities into enterprise applications while optimizing performance, latency, reliability, and cost.
- Evaluate emerging LLM technologies and recommend adoption strategies.
- Architect and implement scalable RAG pipelines utilizing embeddings, vector databases, and semantic retrieval techniques.
- Design and optimize document ingestion, indexing, retrieval, ranking, and generation workflows.
- Improve retrieval relevance, response quality, and contextual accuracy.
- Work with vector stores and search technologies such as:
- Elasticsearch
- OpenSearch
- FAISS
- Other vector database platforms
- Build hybrid search solutions combining semantic and keyword-based retrieval techniques.
- Design and develop multi-agent AI architectures to automate complex workflows and decision-making processes.
- Define agent orchestration frameworks, communication patterns, planning strategies, and tool integration mechanisms.
- Build agent collaboration, memory management, reasoning, and execution frameworks.
- Develop reusable agentic AI platforms and accelerators for enterprise-scale adoption.
- Evaluate and implement emerging best practices in autonomous and collaborative AI systems.
- Design scalable AI services using microservices, APIs, and cloud-native architectural patterns.
- Build highly available, secure, and observable AI platforms.