Fluidata Analytics
Website:
fluidata.co
Job details:
Location Details
Remote, India (US hours: 4PM IST to 12AM IST)
About Us
Fluidata Analytics is a data and AI firm that helps organizations turn fragmented information into decision-ready systems. We design, build, and operate modern data capabilities across strategy, engineering, analytics, governance, managed data teams, and AI integrations. With a growing focus on supply chain decision intelligence through Fluidata OS, we partner with startups and global enterprises to deliver practical, high-quality solutions that are built for adoption and measurable business impact. Our teams are hands-on, collaborative, and focused on moving clients from experimentation to scalable, production-ready outcomes.
Role Introduction
As a Senior AI/ML Engineer at Fluidata Analytics, you will help shape and deliver production-grade AI systems for real business use. This is a hands-on, high-ownership role focused on building LLM-based applications, RAG pipelines, embedding-driven systems, and predictive models that solve complex client challenges. You will work end to end across the ML lifecycle on Databricks and modern cloud platforms, while also partnering directly with client stakeholders to translate business goals into sound technical solutions. If you enjoy combining deep technical execution with architectural thinking and client-facing problem solving, this role offers the opportunity to make a visible impact.
What You'll Do
- Design and deliver production-ready AI/ML solutions, with a strong focus on LLM applications, RAG workflows, embeddings, and predictive modeling.
- Own end-to-end ML delivery, including data pipelines, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.
- Architect and optimize generative AI systems that integrate enterprise data, vector search, orchestration frameworks, APIs, and business workflows.
- Partner with US-based clients to understand business needs, define success metrics, communicate trade-offs, and turn requirements into actionable implementation roadmaps.
- Establish best practices for experimentation, MLflow tracking, model versioning, CI/CD, observability, governance, and reliable production operations.
- Collaborate closely with data engineers, analytics teams, and stakeholders to build scalable data and AI products that are accurate, usable, and maintainable.
- Provide technical leadership through design reviews, mentoring, and guidance on architecture, model quality, performance, cost, and latency trade-offs.
What You Bring
- Strong industry experience in applied machine learning, AI engineering, or data science, with the scope and ownership expected of a senior individual contributor.
- Proven hands-on experience building LLM-based applications, including RAG pipelines, embeddings, semantic search, retrieval optimization, and grounded response generation.
- Deep proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.
- Experience building and operating ML systems on Databricks or similar cloud data platforms, including scalable pipelines, experiment tracking, model lifecycle management, and production deployment.
- Solid understanding of MLOps practices, including MLflow, model registries, CI/CD, monitoring, drift detection, versioning, rollback strategies, and production reliability.
- Strong foundation in classical machine learning and predictive modeling, including classification, regression, forecasting, evaluation, and model performance tuning.
- Excellent communication and consulting skills, with the ability to translate business problems into technical solutions and work effectively with distributed teams and client stakeholders.
Nice to Have
- Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
- Experience with vector databases, agentic AI workflows, orchestration frameworks such as LangChain, and real-time inference architectures.
- Familiarity with responsible AI practices, including explainability, hallucination mitigation, bias awareness, and compliance-conscious data handling.
- Background in consulting or enterprise-facing delivery across multiple client environments and business domains.
- Exposure to supply chain, operations, or decision intelligence use cases.
If you're excited by the challenge of building scalable, reliable AI systems that clients actually use, we’d love to hear from you.
Click on Apply to know more.