🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
BCI has an open position on our GenAI team working with our USA based client. The Sr. LLM Fine Tuning Engineer will join our offshore development team that is growing and there is a lot of new and exciting GenAI work to be completed. This is a full-time remote position and must be able to work blended hours of EST / IST timings. Please note this is a Sr level role focused 100% on leading LLM Fine tuning About the Role We're looking for an LLM Fine Tuning Engineer to lead the customization and optimization of large language models for production use cases. This role owns the full fine-tuning lifecycle from data preparation through training, evaluation, and deployment working with open-weight models (e.g., Llama, Gemma) as well as proprietary/managed models (e.g., Google Gemini) where fine-tuning access is available. You'll be hands-on with real training runs at scale, not just prompt engineering, or API integration. What You'll Do Fine-tune and adapt large language models (Llama, Gemma, and other open-weight models, plus managed options like Gemini where applicable) for specific business use cases Design and execute full fine-tuning pipelines: dataset curation and cleaning, tokenization, training/eval splits, hyperparameter selection, and training runs (full fine-tune, LoRA/QLoRA, PEFT, RLHF/DPO as appropriate) Run and manage large-scale training jobs across multi-GPU / distributed environments Evaluate model performance using both automated benchmarks and human-in-the-loop review; iterate to close quality gaps Optimize models for production inference (quantization, distillation, latency/cost tradeoffs) Deploy fine-tuned models into production systems and monitor performance, drift, and degradation over time Collaborate with data, ML infrastructure, and product teams to define fine-tuning objectives and success metrics Stay current on the open-model landscape and evaluate new base models as candidates for fine-tuning Document methodology, training runs, and results for reproducibility and knowledge sharing Required Qualifications Strong proficiency in Python, with solid software engineering fundamentals (not just notebooks) Hands-on, production-level experience fine-tuning LLMs this is a must-have, not exploratory/academic experience only Demonstrated experience taking fine-tuned models into live, large-scale production systems (not just POCs) AWS and /or Google cloud production experience will be considered Experience with open-weight model families (e.g., Llama, Gemma, Mistral, or similar) Practical knowledge of fine-tuning techniques: LoRA/QLoRA, PEFT, full fine-tuning, instruction tuning, RLHF/DPO Experience with ML/training frameworks such as PyTorch, Hugging Face Transformers/TRL/PEFT, DeepSpeed, or similar Familiarity with distributed/multi-GPU training and the associated infrastructure challenges Solid understanding of model evaluation methodology for generative models Nice to Have Experience fine-tuning or customizing Google Gemini or other managed/API-based models Experience with vector databases, RAG architectures, or hybrid RAG + fine-tuning approaches Experience with MLOps tooling for training pipelines (e.g., MLflow, Weights & Biases, Kubeflow, SageMaker, Vertex AI) Experience with model quantization and inference optimization (vLLM, TensorRT-LLM, GGUF, etc.) Background in NLP research or publications related to LLM training/fine-tuning What Success Looks Like Within your first few months, you're independently running fine-tuning jobs on open models, have a clear point of view on which base models and techniques fit which use cases, and have shipped at least one fine-tuned model into a production system with measurable quality improvement over baseline. Interview Process: If profile appears to fit role, we will send you a request for more information and details on your background. 2. Initial 30 min MS Teams conversation with BCI-IT team to go over your hands on experience and determine fit. 3. If potential fit, you will be sent a video technical screen with 8 questions on Python, GenAI and LLM fine tuning. 4. 45 min to 1 hour client technical interview with Python code share activity. You will be speaking with 2-3 Sr. team members. Hiring decision can be made after call. .
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.