Senior ML Engineer – LLMs

Job Title: Senior ML Engineer – LLMs

Company: Hodos 360

Location: Bangalore (Work From Office – WFO)

Shift: 5:00 PM to 2:00 AM IST

Experience: 3–8 years

Hodos 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.

As 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.

• Collaborate 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-chaining
• Build robust data pipelines for:
• Data collection, cleaning, labeling, and augmentation
• Synthetic data generation using LLMs where appropriate
• Evaluate model performance using:
• Quantitative metrics (accuracy, BLEU/ROUGE, relevance, latency, etc.)
• Human evaluation, rubric-based scoring, and A/B tests
• Integrate LLMs into applications via APIs or on-prem deployments:
• Work with REST/gRPC APIs, vector databases, and orchestration layers
• Implement and optimize retrieval-augmented generation (RAG) pipelines:
• Indexing business documents into vector DBs
• Designing retrieval strategies for grounded responses
• Optimize 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.

• 3–8 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/JAX
• Hugging Face ecosystem (Transformers, Datasets, Accelerate, PEFT)
• Solid understanding of:
• Transformer architectures and attention mechanisms
• Fine-tuning methods, transfer learning, and PEFT techniques
• Classic NLP concepts (tokenization, embeddings, sequence models)
• Practical experience in:
• Fine-tuning or adapting LLMs on custom datasets
• Building RAG systems with vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS, pgvector)
• Prompt engineering and system prompt design for business use cases
• Experience with MLOps and productionization:
• Docker, basic CI/CD, model deployment (REST APIs, serverless, etc.)
• Monitoring model performance and drift, logging, and observability
• Familiarity 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.

• Experience with:
• RLHF / DPO or other preference optimization techniques
• Low-latency inference, quantization (e.g., 4-bit, 8-bit), and GPU optimization
• Agent frameworks or orchestration tools (LangChain, LlamaIndex, etc.)
• Prior work on:
• Chatbots, AI copilots, or internal automation assistants
• Multi-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.

• 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.