AI Forward Deployed Engineer

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AI Forward Deployed Engineer

About the Role

We are looking for an AI Forward Deployed Engineer to work directly with our enterprise customers in designing, building, and deploying cutting-edge AI solutions that solve real business problems. This is a highly hands-on role that combines AI engineering, software engineering, customer engagement, and solution architecture. You will work closely with business stakeholders to understand their domain, rapidly prototype AI applications, and deliver production-ready AI products using the latest Agentic AI technologies, Large Language Models (LLMs), and cloud-native platforms. If you enjoy solving complex business challenges, building AI-native products, and working at the intersection of consulting and engineering, we’d love to hear from you

Key Responsibilities

Partner with business and technical stakeholders to understand business objectives, domain challenges, and identify high-impact AI use cases.
Gain a deep understanding of customer business processes, domain knowledge, data landscape, and enterprise architecture to design effective AI solutions.
Translate requirements into technical design solutions.
Rapidly develop AI prototypes and proof-of-concepts that demonstrate business value and accelerate customer adoption.
Design, develop, and deploy production-grade AI applications using modern Agentic AI frameworks such as LangGraph, LangFuse, MCP, AI gateways such as LiteLLM, and fit-for-purpose Large Language Models (LLMs).
Build scalable AI solutions on enterprise cloud platforms including Microsoft Azure, Azure OpenAI, Azure Kubernetes Service (AKS), Databricks, and related technologies.
Develop end-to-end AI products including backend services, APIs, agent orchestration, frontend applications, and enterprise integrations.
Build custom AI tools, functions, connectors, retrieval pipelines, and agent capabilities required for intelligent workflows.
Implement enterprise-grade software engineering practices including testing, CI/CD, security, scalability, observability, and architecture standards.
Design and implement evaluation frameworks, monitoring, tracing, logging, and observability capabilities to continuously measure and improve AI agent performance.
Optimize AI applications for quality, reliability, latency, scalability, cost, and responsible AI practices.
Collaborate closely with architects, data engineers, platform teams, and customer stakeholders throughout the solution lifecycle.
Produce high-quality technical documentation, architecture artifacts, and knowledge transfer materials.

Required Qualifications

Bachelor’s or master’s degree in computer science, artificial intelligence, software engineering, data science, or a related field.

3+ years of software engineering experience with strong proficiency in Python.
Good knowledge and experience with Docker containers and Kubernetes platforms
Hands-on experience building Generative AI or LLM-powered applications.
Experience with Agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or similar technologies.
Strong understanding of Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI orchestration, and tool/function calling.
Experience developing REST APIs, backend services, and cloud-native applications.
Experience working with Microsoft Azure, Azure OpenAI, Databricks, Kubernetes (AKS), or equivalent cloud platforms.
Strong understanding of software engineering best practices, including testing, version control, CI/CD, and application architecture.
Experience engaging directly with customers and translating business requirements into technical solutions.
Excellent analytical, communication, and problem-solving skills.
Ability to thrive in fast-paced consulting and customer-facing environments.

Nice-to-Have Skills

Experience working with modern enterprise data platforms such as Microsoft Fabric, Databricks, Snowflake, or equivalent cloud data platforms.
Experience integrating AI solutions with enterprise applications and business systems such as SAP, Salesforce, ServiceNow, Microsoft 365, or similar enterprise platforms.
Understanding of open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi, and their role in modern data architectures.
Experience with unstructured document processing and parsing frameworks/libraries such as Docling, Unstructured.io, LlamaParse, Azure AI Document Intelligence, or similar tools.
Experience implementing observability and monitoring solutions using Grafana, Prometheus, OpenTelemetry, or similar monitoring stacks.
Familiarity with event-driven architectures, message brokers, and streaming technologies such as Kafka or Azure Event Hubs.
Experience working in Agile delivery environments with cross-functional product and engineering teams.
Contributions to open-source projects, technical blogs, conference talks, or AI community initiatives.
Relevant cloud or AI certifications (Microsoft Azure AI Engineer, Azure Developer Associate, Databricks Gen AI Engineer, Kubernetes Certified Developer, or similar).

About the Company

EnablerMinds is a next-generation boutique company delivering end-to-end Cloud, Data & AI solutions. Our elite team of data engineers, architects, data scientists, AI engineers and industry specialists empowers enterprises to modernize their data ecosystems, accelerate AI adoption, and unlock transformative business value. With proven methodologies, enterprise-ready frameworks, and an agile delivery and staffing model, we deliver high-performance, scalable, and cost-efficient outcomes tailored to the needs of tomorrow’s intelligent enterprise. We welcome applicants of all genders, backgrounds and identities.