Data Scientist

Job Title: AIML Engineer

The AI Engineer is a hands-on technical role within our growing practice. This position focuses on building and implementing production-grade AI solutions that leverage the latest technology, agentic architectures, and cloud-native infrastructure, with a strong focus on natural language interfaces and data democratization through conversational insights. You will work as part of a technical team to translate business requirements into scalable, reliable AI systems.

We are looking for a deeply technical individual with strong hands-on engineering expertise who is passionate about building AI systems, including semantic layer architecture and SQL generation quality. You'll collaborate with senior engineers and clients to implement production-ready solutions while continuously learning and applying best practices.

Responsibilities:

Build and implement enterprise-scale AI systems including agentic workflows, RAG architectures, conversational analytics platforms, and text-to-SQL solutions

Develop and implement semantic layers that enable accurate natural language to SQL translation across complex enterprise data warehouses in Databricks, Snowflake, or AWS platforms

Contribute to MLOps/DevOps practices for AI systems including CI/CD pipelines, infrastructure as code, automated testing frameworks, and production monitoring solutions

Create robust evaluation frameworks including golden datasets for text-to-SQL accuracy, agent quality metrics, and comprehensive system performance benchmarks

Implement data architectures for AI systems including knowledge base design, vector databases, retrieval optimization, semantic modeling, and real-time data pipelines

Build conversational insights systems following proven methodologies: requirements gathering, semantic layer implementation, evaluation framework creation, and client handoff

Support technical client engagements as an engineering contributor for complex implementations, providing hands-on development for both traditional AI and conversational analytics deployments

Apply engineering standards for prompt engineering, SQL generation quality, model selection, and guardrails implementation that ensure consistent, high-quality AI experiences

Experience:

3-5 years of software engineering experience with at least 2 years focused on AI/ML systems in production environments, preferably including text-to-SQL or conversational analytics implementations

Hands-on experience building LLM-based applications including prompt engineering, RAG implementations, multi-agent systems, and natural language interfaces for data

Experience with cloud platforms (AWS/Azure/GCP) with specific experience in Databricks (Unity Catalog, Genie) or Snowflake (Cortex, Native Apps) highly preferred

Background in MLOps practices and data engineering including semantic layer design, SQL optimization, and evaluation pipeline implementation

Experience with modern AI stack including vector databases, orchestration frameworks (LangChain, LlamaIndex), and specialized evaluation tools for conversational AI quality

Experience building high-throughput, low-latency systems that handle enterprise scale, including text-to-SQL systems with high accuracy rates

Production experience with multiple LLM providers and understanding of their trade-offs for various use cases including SQL generation

Ability to translate complex business requirements into technical implementations and collaborate effectively with cross-functional teams

Qualifications:

Education:

Bachelor's degree in Computer Science, Engineering, or related field

Technical Skills:

Strong Python programming and software engineering best practices

Strong SQL proficiency and experience with query optimization across multiple platforms

Experience with semantic layer tools (Unity Catalog, AWS Glue Data Catalog) and metadata management

Experience with containerization (Docker, Kubernetes) and microservices

Proficiency in infrastructure as code (Terraform, CloudFormation)

Familiarity with front-end technologies for full-stack AI applications

AI/ML Expertise:

Solid understanding of transformer architectures and LLM capabilities/limitations

Experience with text-to-SQL evaluation metrics (execution accuracy, semantic correctness)

Knowledge of fine-tuning approaches and retrieval-augmented generation (RAG)

Understanding of AI safety, bias mitigation, and responsible AI practices

Additional Assets:

Experience building golden datasets and evaluation frameworks for conversational AI

Contributions to open-source projects or technical blog posts on AI/ML topics

Specific experience with conversational insights in enterprise settings

Background in data analytics or business intelligence

What Success Looks Like:

• In this role, you'll be measured on your ability to deliver high-quality, production-ready AI system components that meet our standards for quality, reliability, and scalability. For conversational analytics implementations, this includes contributing to systems that achieve high accuracy on text-to-SQL evaluations and successfully democratize data access for business users. You'll grow your technical expertise while ensuring every project you contribute tofrom agentic systems to conversational interfacesis implemented successfully.
• If you are interested in this opportunity, then please share your salary expectations over the email along with latest resume.