Senior AI Engineer

Job Description

Job Description

Position Summary
We are seeking a highly experienced Senior AI Solutions Engineer to design, develop, and deploy production-grade AI and Generative AI solutions. The ideal candidate will have strong backend/full-stack engineering expertise, hands-on experience building LLM-powered applications, RAG architectures, agentic workflows, and enterprise-scale data platforms such as Snowflake, Databricks, and BigQuery.
This role requires direct collaboration with business stakeholders, product teams, and customers to translate business problems into scalable AI-driven solutions deployed in production environments.

Mandatory Skills : 8–10+ years backend/full-stack experience
Expert in at least one – Python/ Java / Go / TypeScript and should have API design & integration experience
SQL, Data Pipelines, Snowflake/ Databricks
LLMs/RAG/agentic workflows
Experience working with clients / business stakeholders and product teams directly
AWS/Azure/GCP

Key Responsibilities
AI/GenAI Solution Development

• Design, build, and deploy production-ready AI/LLM applications.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources.
• Build agentic workflows leveraging modern orchestration frameworks.
• Create scalable AI architectures integrating LLMs, vector databases, APIs, and enterprise systems.
• Evaluate and optimize model performance, latency, accuracy, and cost.

Backend & Platform Engineering

• Design and develop scalable APIs and microservices.
• Build robust backend services using Python, Java, Go, or TypeScript.
• Develop integrations with internal and external platforms.
• Implement secure authentication, authorization, and governance controls.

Data Engineering & Analytics

• Design and maintain data pipelines supporting AI applications.
• Work with Snowflake, Databricks, BigQuery, or similar modern data platforms.
• Build ETL/ELT pipelines and data transformation workflows.
• Ensure high-quality data ingestion, processing, and retrieval.

Cloud & DevOps

• Deploy AI solutions on AWS, Azure, or GCP.
• Implement CI/CD pipelines and MLOps practices.
• Monitor production AI systems and optimize infrastructure utilization.
• Ensure scalability, reliability, and observability of deployed solutions.

Client & Stakeholder Engagement

• Partner directly with customers and business stakeholders.
• Gather requirements and translate business challenges into technical solutions.
• Present architecture decisions, trade-offs, and implementation plans.
• Drive projects from prototype through production deployment.

Required Qualifications
Experience

• 8–12+ years of software engineering experience.
• Proven experience delivering customer-facing software solutions.
• Demonstrated experience taking AI solutions from prototype to production.
• Experience working directly with clients, product managers, and business teams.