Senior ML Engineer – LLMs

Job Title: Senior ML Engineer – LLMsCompany: Hodos 360Location: Bangalore (Work From Office – WFO)Shift: 5:00 PM to 2:00 AM ISTExperience: 3–8 yearsAbout Hodos 360Hodos 360 builds AI-driven products and solutions for global businesses, with a strong focus on Large Language Models (LLMs) and automation. We work closely with clients to understand their workflows and deliver customized AI systems that actually get adopted in production.We are looking for a Senior ML Engineer – LLMs who can fine-tune, adapt, and deploy LLM-based solutions for real business use cases.Role OverviewAs a Senior ML Engineer – LLMs at Hodos 360, you will:Design and implement LLM-based solutions tailored to specific client workflows.Fine-tune open-source and proprietary LLMs on domain-specific data.Work closely with product and engineering to take models from prototype to production.Own the full lifecycle: data → training → evaluation → deployment → monitoring.This is a full-time, work-from-office role in Bangalore, aligned to a 5 PM – 2 AM IST shift to support global customers.Key ResponsibilitiesCollaborate with stakeholders to translate business problems into LLM solutions (chatbots, copilots, automation agents, summarizers, etc.).Fine-tune and optimize LLMs using techniques such as:Supervised fine-tuning (SFT)Parameter-efficient fine-tuning (LoRA/QLoRA, PEFT)Prompt engineering and prompt-chainingBuild robust data pipelines for:Data collection, cleaning, labeling, and augmentationSynthetic data generation using LLMs where appropriateEvaluate model performance using:Quantitative metrics (accuracy, BLEU/ROUGE, relevance, latency, etc.)Human evaluation, rubric-based scoring, and A/B testsIntegrate LLMs into applications via APIs or on-prem deployments:Work with REST/gRPC APIs, vector databases, and orchestration layersImplement and optimize retrieval-augmented generation (RAG) pipelines:Indexing business documents into vector DBsDesigning retrieval strategies for grounded responsesOptimize models for cost, latency, and reliability.Collaborate with software engineers to productize models (backend integration, monitoring, logging).Keep track of LLM ecosystem developments and suggest improvements to our stack.Required Skills & Experience3–8 years of hands-on experience in Machine Learning / NLP, with at least 1–2 years focused on LLMs or transformers.Strong programming skills in Python and experience with:PyTorch and/or TensorFlow/JAXHugging Face ecosystem (Transformers, Datasets, Accelerate, PEFT)Solid understanding of:Transformer architectures and attention mechanismsFine-tuning methods, transfer learning, and PEFT techniquesClassic NLP concepts (tokenization, embeddings, sequence models)Practical experience in:Fine-tuning or adapting LLMs on custom datasetsBuilding RAG systems with vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS, pgvector)Prompt engineering and system prompt design for business use casesExperience with MLOps and productionization:Docker, basic CI/CD, model deployment (REST APIs, serverless, etc.)Monitoring model performance and drift, logging, and observabilityFamiliarity with at least one major cloud platform (AWS / GCP / Azure).Strong problem-solving and ability to break down vague business requirements into concrete technical tasks.Comfortable working in a US/overseas-aligned shift (5 PM – 2 AM IST) from our Bangalore office.Good communication skills and the ability to explain technical trade-offs to non-technical stakeholders.Good-to-HaveExperience with:RLHF / DPO or other preference optimization techniquesLow-latency inference, quantization (e.g., 4-bit, 8-bit), and GPU optimizationAgent frameworks or orchestration tools (LangChain, LlamaIndex, etc.)Prior work on:Chatbots, AI copilots, or internal automation assistantsMulti-modal models (text + image / text + structured data)Experience in client-facing roles or consulting, especially for B2B/enterprise AI solutions.Contributions to open-source ML/LLM projects or public demos/portfolio.Why Join Hodos 360?Work end-to-end on real LLM applications that go into production for global businesses.High ownership and the ability to influence our LLM architecture, tooling, and roadmap.Close collaboration with strong engineering and product teams already working heavily with AI.Opportunity to grow into lead or architect roles in the AI/ML space as we scale.