Jobs at TechTorch

Build and deploy production-grade full-stack AI systems for clients and internal accelerators. Responsibilities include data modeling, ETL/ELT pipelines, SCD implementation, dbt transformations, Python/FastAPI services, Next.js applications, AI agent and RAG workflows, orchestration, CI/CD, and AWS/Azure cloud deployments. The role owns projects end to end, translates ambiguous requirements into technical solutions, communicates with business stakeholders, and contributes reusable data and AI assets.

Build production-grade AI systems and full-stack applications from discovery through deployment. Design data models, ETL/ELT pipelines, dimensional schemas, SCD strategies, and data-quality controls. Develop Python/FastAPI services and Next.js frontends, integrate RAG and agentic workflows, orchestrate automation, and manage CI/CD and cloud deployments across AWS and Azure. Translate ambiguous client needs into scalable technical solutions while contributing reusable data and AI accelerators.

Configure, implement, and optimize Salesforce CPQ solutions for clients. Gather business requirements, lead deployments, automate processes with Salesforce tools, ensure best practices, provide client support and training, maintain documentation, and manage multiple projects. The role also involves staying current with Salesforce CPQ features and related tools such as DocuSign and Conga.

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Designs and governs enterprise data architectures supporting analytics, reporting, operations, and AI initiatives. Responsibilities include data modeling, warehousing, integration pipelines, APIs, governance, quality, lineage, security, and architecture optimization. The role provides technical leadership and mentorship, guides cloud and big data technology decisions, develops proofs of concept and roadmaps, and supports client proposals and account growth. Collaboration with engineers, analysts, developers, and business stakeholders is central to delivering scalable, secure, high-performance data platforms.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Lead client-facing AI strategy and enterprise architecture engagements: run discovery workshops, design production-grade AI architectures (LLMs, RAG, agents, vector DBs), define governance and operating models, guide engineering teams, shape AI offerings, and mentor consultants to deliver measurable business outcomes.