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.
