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.