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Position Overview We are hiring a Machine Learning / AI Engineer with 36 years of experience designing and deploying AI systems in production environments. This role requires strong Python expertise, hands-on experience with generative AI architectures, and the ability to productionize LLM driven systems with reliability, observability, and cost efficiency in mind. The ideal candidate can bridge research and engineering, translating AI concepts into scalable systems. Key Responsibilities Design and deploy Retrieval-Augmented Generation (RAG) pipelines using embedding models and vector databases such as FAISS, Pinecone, Weaviate, or Chroma. Develop LLM-powered applications using frameworks such as LangChain, LlamaIndex, AutoGen, or similar orchestration frameworks. Implement agent-based systems that support multi-step reasoning, tool invocation, and structured workflows. Fine-tune or adapt models using techniques such as LoRA (Low-Rank Adaptation) and parameter-efficient fine-tuning (PEFT). Build structured output pipelines using schema validation and function-calling APIs. Develop automated evaluation pipelines, including benchmarking datasets, regression testing for prompts, and output quality scoring. Monitor and optimize latency, token usage, throughput, and cost across AI workflows. Build observability layers for AI systems including logging, tracing, prompt telemetry, and output validation. Collaborate with backend and frontend teams to integrate AI services into production systems via REST APIs or asynchronous workflows. Ensure robustness against hallucinations, prompt injection, and adversarial inputs through validation logic and contextual constraints. Required Technical Skills Advanced proficiency in Python. Hands-on experience with PyTorch and/or TensorFlow. Practical experience with Hugging Face Transformers and modern LLM APIs. Strong understanding of tokenization, embeddings, semantic similarity, and vector search. Experience building and optimizing RAG pipelines. Familiarity with containerization (Docker) and deployment in Kubernetes environments. Experience with MLOps tooling for model versioning, deployment, and monitoring. Strong API design and microservices fundamentals. Preferred Qualifications Experience deploying open-source models in production environments. Experience with distributed training or inference optimization. Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML). Knowledge of evaluation frameworks and human-in-the-loop validation systems. Ideal Candidate Profile Execution-oriented with a strong focus on production readiness. Able to reason deeply about model behavior, trade-offs, and system constraints. Comfortable iterating quickly while maintaining engineering rigor. Collaborative and able to clearly explain complex AI systems to cross-functional teams. Driven by measurable product impact rather than experimentation alone. .
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.