Design, deploy, and scale production-grade machine learning, deep learning, and generative AI solutions on cloud platforms, primarily Azure. Lead LLM applications such as chatbots and agentic workflows, oversee the full ML lifecycle, ensure production reliability and governance, and partner with global business and technical stakeholders. Mentor junior team members, influence architecture and best practices, and drive AI initiatives from problem definition through measurable business impact.
Senior Digital Innovation Engineer – Data Science
Location : Chennai, India
Required Language : English
Employment Type : Full Time
Seniority Level : Mid – Senior level
Travel : 10 %
Required Language : English
Employment Type : Full Time
Seniority Level : Mid – Senior level
Travel : 10 %
Buckman is a privately held, global specialty chemical company with headquarters in Memphis, TN, USA, committed to safeguarding the environment, maintaining safety in the workplace, and promoting sustainable development. Buckman works proactively and collaboratively with its worldwide customers in pulp and paper, leather, and water treatment to deliver exceptional service and innovative specialty chemical solutions to help boost productivity, reduce risk, improve product quality, and provide a measurable return on investment. Buckman is in the middle of a digital transformation of its businesses and focused on building the capabilities and tools in support of this.
Role Summary
We are seeking a Senior Data Scientist with strong hands-on experience in building, deploying, and scaling machine learning and deep learning solutions on cloud platforms (preferably Azure). The ideal candidate will have practical experience working with Large Language Models (LLMs)—either as chatbots, copilots, or agentic AI systems—and a proven track record of delivering production-grade AI solutions. We are looking for someone who is capable of owning end-to-end AI solutions, mentoring team members, and collaborating with global stakeholders to create measurable business impact.
Basic Qualifications (Must-Have)- Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field
- 7+ years of hands-on experience in data science, machine learning, or applied AI roles
- Strong experience in:
- Designing, training, and deploying ML/DL models into production
- Cloud-based ML workflows, preferably on Microsoft Azure
- Python-based ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, etc.)
- Proven experience with:
- End-to-end ML lifecycle (data ingestion → modeling → deployment → monitoring)
- Model performance evaluation, retraining strategies, and production stability
- Demonstrated success through:
- Production deployments, internal tools, or a verifiable portfolio of AI problems solved
Preferred Qualifications (Strong Advantage)
- Master’s or PhD degree in Data Science, Computer Science, AI, Statistics, Engineering, or related field
- Hands-on experience with Generative AI / LLMs, including:
- Chatbots, copilots, or conversational AI systems
- Agentic architectures (tool calling, memory, orchestration, RAG pipelines, multi-step reasoning)
- Experience using or deploying:
- Azure OpenAI, OpenAI, or similar LLM platforms
- Vector databases, embeddings, prompt engineering, and retrieval-based systems
- Experience with MLOps / LLMOps, including:
- CI/CD for ML
- Model versioning, monitoring, and observability
- Azure ML, Azure DevOps, or similar platforms
- Prior exposure to:
- Specialty chemicals, manufacturing, supply chain, logistics, or industrial analytics
- Engineering background (especially Chemical Engineering)
- Experience:
- Setting up or scaling a data science program
- Working with global or cross-functional stakeholders
- Working in a startup or fast-paced product environment
Leadership & Ownership (Added Emphasis)
- Experience mentoring junior data scientists or ML engineers
- Ability to own AI initiatives end-to-end, from problem framing to business impact
- Experience influencing technical decisions, architecture, or best practices
- Comfort working independently while collaborating with product, IT, and business teams
Core Responsibilities
- Design, build, and deploy production-grade ML/DL and GenAI solutions
- Lead development of LLM-based applications, including chatbots and agentic workflows
- Architect scalable cloud-based AI solutions using Azure services
- Partner with business stakeholders to translate real-world problems into AI solutions
- Ensure reliability, performance, and governance of AI systems in production
- Contribute to best practices in ML engineering, GenAI architecture, and MLOps
- Support adoption, documentation, and long-term maintainability of AI platforms
Personality Traits & Soft Skills
- Strong business mindset with a bias for execution and measurable impact
- High ownership and accountability for outcomes, not just models
- Excellent collaboration skills across data, engineering, and business teams
- Clear communicator with the ability to explain complex AI concepts to non-technical audiences
- Strong planning and organizational skills; able to manage multiple initiatives
- Continuous learner, especially in fast-evolving areas like GenAI and agentic AI
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