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ConnectWise is building AI features into the IT management platform its partner firms use for monitoring, ticketing and service delivery. This role designs and ships them: retrieval augmented generation, agentic workflows and what the company calls Modular Cognitive Processes. The stack is Python with FastAPI or Flask for model serving, LangChain, LlamaIndex, Haystack or Hugging Face on the framework side, vector stores such as OpenSearch, Pinecone or Weaviate, and cloud services on AWS, Azure or GCP. Read the years carefully, because this posting contradicts itself. The role summary says four or more years, while the skills list says two to five and the education line says two or more. We publish the higher figure, but a candidate at two or three years should apply anyway, because the posting itself sets that bar twice. Who it is for: THE YEARS CONTRADICTION, STATED PLAINLY This posting gives three different figures. The opening summary calls it "The Senior AI Engineer (4 + years of experience)". The skills list asks for "2-5 years of experience in AI/ML engineering". The education line asks for "2+ years of relevant experience". We have published it at 4 because that is the highest stated figure and we would rather not understate a bar, but if you have two or three years of real AI or ML engineering, two of the three statements in this posting include you. Apply and let them level you. WHAT THE POSTING ASKS FOR - Familiarity with AI and ML concepts and application development using generative AI. - Strong Python with a solid foundation in object oriented programming and software engineering principles. - Hands on experience with LangChain, LlamaIndex, Haystack or Hugging Face. - CI/CD tools and workflows: Git, Docker, Jenkins, Airflow. - Exposure to AWS, Azure or GCP and services such as S3, SageMaker or Vertex AI. - Understanding of vector databases (OpenSearch, Pinecone, Weaviate) and embedding techniques. - SQL and NoSQL databases and data manipulation. - A bachelor's or master's degree in computer science or a related field. WHAT THE JOB ACTUALLY IS DAY TO DAY Designing and deploying AI applications using RAG and agentic frameworks, building and optimising machine learning models, maintaining scalable data pipelines behind AI workflows, and implementing REST APIs in FastAPI or Flask for model integration. You will work with embeddings and LLMs including GPT-4, Claude and Mistral for retrieval and reasoning, debug AI systems across the stack, validate components with pytest or unittest, and document designs and research findings. LOCATION AND WORKING PATTERN Bengaluru. The posting states work type as hybrid. HONEST FIT GUIDANCE This is a build role, not a research role. If you have shipped a RAG or agent system into production in Python and can reason about retrieval quality and latency, the title's "Senior" is less of a wall than it looks. .
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.