Forward Deployed Engineer

Key Responsibilities

1. Business Embedding and Outcome Ownership

• Embed with business and engineering teams to own AI outcomes within a defined business domain.
• Build and deliver AI solutions hands-on; this is an execution role, not an advisory role.
• Convert AI potential into production value through code-first delivery and active repository contributions.

2. Problem Discovery and Solution Design

• Understand business processes, pain points, systems, data flows, and success metrics.
• Translate problems into MVPs, integrations, automations, and production-ready solutions with an ownership mindset
• Build across APIs, databases, cloud platforms, workflow tools, enterprise systems, and AI/GenAI technologies.

3. Rapid Prototyping and Value Validation

• Own the journey from discovery to working solution, rapidly proving business value through pilots and POCs

4. Integration, Adoption, and Scale

• Integrate with enterprise platforms, data systems, workflows, collaboration tools, and third-party APIs.
• Document architectures, implementation playbooks, reusable components, and customer-specific solution guides.
• Feed field learnings into product roadmap, accelerators, and go-to-market propositions.

Required Skills and Experience

• 4–8 years of experience in AI led engineering, implementation, product, consulting, or customer-facing technology roles.
• Strong engineering fundamentals with hands-on coding experience in Python, JavaScript/TypeScript, Java, .NET/C#, or Go.
• AI proficiency is mandatory; candidates may come from software engineering, data science, UX, or related domains with proven hands on experience.
• Daily AI tool usage, demonstrable code contributions, and documented token usage.
• Strong analytical thinking and expertise in effectively utilizing data to derive AI solutions to solve business problems.
• Strong understanding of APIs, databases, cloud services, authentication, integrations, and deployment.
• Experience in Data and analytics platforms.
• Comfortable with structured and unstructured data.
• Experience with GenAI, LLMs, RAG, agents, AI workflow automation, prompt engineering, model integration and model training.
• Cloud experience across AWS, Azure, or Google Cloud.
• Good communication, adaptability, and problem-solving in ambiguous environments.

Good to Have

• Knowledge of ML algorithms, model building, deployment, deep learning, and NLP.
• Experience integrating with Salesforce, Jira, Rally, Oracle, ServiceNow,  Microsoft Dynamics or similar platforms.
• Familiarity with data engineering, ETL/ELT pipelines, BI dashboards, analytics, and reporting workflows.
• Healthcare exposure, especially contact centers, claims automation, finance, or technology services.