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About Aubrant Aubrant Digital is a leader in multi-shore custom application development. We are passionate about solving our clients business problems through consultative teamwork, innovative software, and proven processes. Weve served more than 50 clients and delivered hundreds of high quality, custom enterprise applications. Our clients value us as integral team members who get the job done on time and on spec, and we are proud of our high client retention rate and under 2% staff turnover. With offices in New Jersey, Boston, Costa Rica, and Eastern Europe, we execute the full software lifecycle, from architecture and design through development, QA and application maintenance & support. Our company culture emphasizes client service, trust-based relationships, and innovation. Overview: Were seeking an experienced strong Generative AI Engineer to support enterprise-wide AI enablement initiatives. This role will focus on building robust GenAI and agentic AI workflows, automating workflows and working across diverse platforms like AWS, Microsoft 365, MS Copilot, and other 3rd party GenAI platforms and libraries. The ideal candidate is highly self-driven and comfortable operating across architecture, and hands-on implementation. This is a high-impact role supporting an AI Center of Excellence (CoE) at scale. Experience: Over 10 years of robust software engineering experience, including 2 to 3 years of dedicated hands-on work in Generative AI and Retrieval-Augmented Generation (RAG) architecture Over 3 years of cloud native experience preferably on AWS. Over 5 years of hands-on experience with Python Experience building interoperable Agentic AI Proof of Concepts using Model Context Protocol (MCP) and/or Google A2A. Key Responsibilities: Design and develop GenAI solutions using prompt engineering, Retrieval-Augmented Generation (RAG), and custom pipelines Design and develop interoperable AI agents using Model Context Protocol (MCP) and Google A2A Automate workflows involving parsing unstructured content such as emails, documents, and web pages in to highly accurate and reliable structured content Automate building documents using data and content from various diverse sources Build enterprise-wide reusable services and components Design and build MCP hosts, clients and servers Establish frameworks for automated LLM testing Create regression test suites to detect drift or prompt breakage Integrate with internal and external web services using secure authentication and authorization mechanisms Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines Experience with agile methodologies and ability to independently document user stories in the absence of Business Analyst Collaborate with the AI CoE team to ensure scalability, reusability, and alignment with governance standards Required Skills: Very strong Python skills. Strong hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python based frameworks Strong hands-on experience in prompt engineering, context construction, grounding strategies Strong hands-on experience with Retrieval Augmented Generation (RAG) extracting, chunking and create embeddings from unstructured documents from diverse sources including O365(email, word, excel), PDFs, and webpages. Comfortable building Model Context Protocol (MCP) clients, servers and hosts. Strong Expertise in building REST APIs and integrating with internal/external APIs Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents Knowledge of Google A2A Deep experience in AWS (Lambda, Bedrock, Step Functions, API Gateway, IAM) Strong experience with monitoring using LangSmith, CloudWatch, or other similar GenAI observability tools Excellent GenAI foundations and concepts Clear understanding of enterprise data privacy, AI governance, and observability Nice to Have: Experience with Microsoft Copilot extensibility (Graph connectors, plugins, or adaptive cards) Ability to build and orchestrate AI-to-AI interactions using Google A2A, integrating multiple agents or tools Strong traditional ML Experience Success Profile: Youre a builder who can translate fuzzy business needs into elegant GenAI and Agentic AI workflows Youre a problem solver whos comfortable navigating across cloud platforms, tools, and data sources Youre collaborative, with the ability to work independently while aligning with enterprise AI goals .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.