Company Description:
About Sutherland
Artificial Intelligence. Automation. Cloud engineering. Advanced analytics. For business leaders, these are key factors of success. For us, they’re our core expertise. We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence.
We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model.
For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA. We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships.
Sutherland
Unlocking digital performance. Delivering measurable results.
Job Description:
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Serve as the lead AI & data strategist, discovering and onboarding new data sources and shaping end-to-end pipelines that span traditional analytics, machine learning (ML), deep learning (DL), natural-language processing (NLP), generative AI (GenAI), and agentic AI.
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Partner with engineering to design, build, and harden production-grade data and AI products(e.g., RAG search, LLM-powered assistants, anomaly-detection services).
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Run analytical experiments—classification, forecasting, LLM fine-tuning, prompt-engineering A/B tests—to solve business problems across multiple domains.
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Collect, clean, and enrich large structured, semi-structured, and unstructured datasets (text, image, audio, sensor, graph).
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Design, train, and validate classical ML models, DL architectures (CNN, RNN, Transformers), and instruction-tuned LLMs; measure drift and conduct error analysis to drive iterative improvement.
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Implement retrieval-augmented generation and agentic task-planning pipelines that combine vector search, function calling, and tool invocation.
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Document findings, publish reusable components, and mentor junior data scientists and ML engineers.
Qualifications:
Experience
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10+ years delivering data-driven solutions, with demonstrable depth in at least two of: ML, DL, NLP, GenAI, agentic AI.
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Hands-on proficiency in Python(pandas, NumPy, scikit-learn), DL frameworks(TensorFlow or PyTorch), and LLM / GenAI toolkits(Hugging Face Transformers, LangChain/LangGraph, Google ADK).
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Proven track record in feature engineering, pattern recognition, predictive modeling, and LLM fine-tuning / evaluation.
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Experience optimizing GPU / TPU workloads and scaling distributed training or inference.
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Familiarity with vector databases(FAISS, Chroma, Pinecone) and distributed data systems (Spark, BigQuery, Snowflake).
Skills
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Ability to architect highly scalable, fault-tolerant AI systems using open-source and cloud-native components.
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Strong understanding of prompt design, grounding external knowledge, agent orchestration, and chain-of-thought safety controls.
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Comfortable working in fast-moving, research-oriented teams with multiple concurrent projects.
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Excellent problem-solving, critical-thinking, and communication abilities; capable of translating complex AI concepts into clear business value.
Education
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Bachelor’s degree (or equivalent) in Computer Science, Information Technology, Data Science, or another quantitative field.
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Graduate degree or notable certifications in AI / ML / DL / NLP are a plus.
Additional Information:
All your information will be kept confidential according to EEO guidelines.
