Location Details
India (Remote)
About Us
Fluidata Analytics is an India-based data, analytics, and AI consultancy founded in 2020, dedicated to helping organizations build robust data foundations and turn complexity into clarity. We partner with fast-growing startups and global Fortune 500 companies across more than eight industries, delivering vendor-agnostic solutions that are tailored to each client's unique context.
We work across the full data lifecycle: from data strategy and architecture design, to engineering and integration, to advanced analytics, governance, and enterprise AI. Our teams design modern data platforms, build real-time analytics capabilities, and develop AI solutions that move businesses from reactive reporting to proactive, insight-led decision-making.
Our vision is to become a globally recognized, best-in-class data-focused services firm by consistently shipping high-quality, highly adoptable solutions. We pride ourselves on being hands-on, collaborative, and deeply invested in real-world impact—not just proofs of concept. If you care about building AI systems that actually get used and create measurable business value, you'll feel at home here.
Role Introduction
As a Senior AI/ML Engineer at Fluidata Analytics, you will sit at the intersection of cutting-edge AI research and real-world business problems.
You'll lead the design and delivery of advanced AI solutions, with a strong focus on large language models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, and predictive modelling—often on top of Databricks and modern data platforms. This role combines deep hands-on building with technical leadership: you'll own end-to-end ML workflows, guide architectural decisions, and mentor engineers and data scientists while working directly with client stakeholders.
You'll help our clients move beyond experimentation to production-grade AI. That means building scalable, reliable, and governable systems; implementing agentic and generative AI workflows; and ensuring models are monitored, explainable, and aligned with business objectives. If you enjoy turning promising ideas into robust, production-ready AI solutions on real data at scale, this role is for you.
What You'll Do
- Lead the design, development, and optimisation of AI/ML models—including LLMs, RAG pipelines, embeddings, and predictive models—for complex, real-world client problems.
- Build end-to-end ML workflows on platforms like Databricks, from data ingestion and feature engineering to training, evaluation, deployment, and monitoring.
- Design and implement generative AI and agentic workflows that integrate LLMs with enterprise data sources, APIs, and existing business processes.
- Collaborate with data engineers, BI developers, and client stakeholders to prepare large, high-quality datasets and integrate models into production applications and data products.
- Establish and promote AI/ML best practices around experimentation, reproducibility, versioning, performance tracking, and model governance across projects.
- Mentor and guide junior team members, review code and solution designs, and provide technical leadership during architecture and strategy discussions.
- Engage with clients to understand business objectives, translate them into AI/ML solution roadmaps, and communicate progress and results clearly to both technical and non-technical audiences.
What You Bring
- 4–7 years of industry experience in applied machine learning or data science, or 3–5 years combined with a recent Ph.D. in Computer Science, AI, Machine Learning, Statistics, or a related field.
- Strong proficiency in Python and core ML/AI libraries and frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers).
- Hands-on experience building and deploying LLM-based solutions, including embeddings, RAG architectures, and generative or conversational AI applications.
- Proven track record of developing predictive models for classification, regression, or forecasting, with solid understanding of algorithms, statistical modelling, and optimisation techniques.
- Significant experience working on Databricks or similar cloud-based data and ML platforms, including notebooks, ML runtimes, and scalable data processing.
- Exposure to MLOps practices and tools (e.g., MLflow, model registries, CI/CD, containerization) and deploying models to production in AWS, GCP, or Azure environments.
- Excellent analytical and problem-solving skills, with the ability to work independently, collaborate in distributed teams, and communicate complex concepts clearly to diverse stakeholders.
Nice to Have
- Master's or Ph.D. in a quantitative discipline (Computer Science, Machine Learning, Statistics, Mathematics, or related field).
- Experience with building agentic AI workflows, tools, or orchestration frameworks for complex multi-step tasks.
- Familiarity with broader programming ecosystems such as R or C++ for performance-critical or legacy integration scenarios.
- Background in data-intensive consulting, analytics services, or working directly with enterprise clients across multiple domains.
- Experience implementing responsible AI practices, including model explainability, bias detection/mitigation, and compliance-aware data handling.
- Hands-on experience with feature stores, vector databases, and real-time inference setups.
- Contributions to open-source ML/AI projects, publications, or technical blogging/speaking are a plus.
If you're excited about building practical, scalable AI systems that make data truly usable, we'd love to hear from you—apply now and help shape the next generation of enterprise AI at Fluidata Analytics.
