Ai Ml Engineer

Codemonk; Bengaluru; On-site; INR 1,500,000 - 3,500,000; Full time

Job summary

Solutions By Text is seeking an AI Engineer (Python, GenAI/LLMs + ML Fundamentals) in Bengaluru, India, to own full ML lifecycle from data curation to deployment. You will build scalable MLOps pipelines, operate model registries, and optimize inference for latency and cost while exploring agentic systems and RAG stacks.

The role requires 3+ years in AI/ML with production deployments, strong Python, and hands-on experience with PyTorch, Hugging Face, and LangChain ecosystems.

Qualifications

• 3+ years hands-on AI/ML engineering with production deployments.
• Strong Python and software engineering fundamentals.
• Deep learning, NLP, transformer architectures and modern NLP tech.
• Experience with LangChain/LangGraph and vector stores.
• Familiarity with MLOps, CI/CD, Docker/Kubernetes and cloud platforms.

Responsibilities

• End-to-end ML ownership from data curation to retraining for predictive and generative systems.
• Build scalable ML pipelines, model registry, A/B testing, drift detection, and automated retraining.
• Fine-tune and deploy open/closed models using LoRA/QLoRA, PEFT and RLHF/DPO.
• Design agentic frameworks, RAG pipelines, tool calling, memory, and planning loops.
• Develop evaluation frameworks and mitigate bias, drift, and hallucinations.
• Track SOTA research and demonstrate work to internal stakeholders.

Skills

Python

Deep Learning

NLP

MLOps

Cloud experience

Docker/Kubernetes

Software engineering fundamentals

Education

B.Tech or M.Tech in Computer Science or AI/ML related field

Portfolio of open-source work or production deployments

Tools

PyTorch

Hugging Face ecosystem

LangChain / LangGraph / LlamaIndex

Vector stores (Pinecone / Weaviate / Qdrant / FAISS)

LLM serving (vLLM / Triton / TGI)

Job description

Job Specific Duties and Responsibilities

• End-to-end ML ownership: Drive the complete lifecycle data curation, model building, evaluation, deployment, monitoring, and retraining for both predictive and generative AI systems.
• Production-grade MLOps: Build scalable pipelines for training, CI/CD, model registry, A/B testing, drift detection, and automated retraining. Optimize inference for latency, throughput, and cost.
• LLMs and SLMs: Fine-tune and deploy open and closed models using techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference tuning (RLHF/DPO). Apply quantization and distillation where needed.
• Agentic systems: Design and productionize agentic frameworks RAG pipelines, tool/function calling, memory, planning loops, and multi-agent orchestration with appropriate guardrails and observability.
• Quality and trust: Build evaluation frameworks (offline + online, including LLM-as-judge and red-teaming). Diagnose and mitigate hallucinations, bias, and drift.
• Rapid innovation: Track SOTA research, prototype quickly, and showcase work through demos and tech talks to internal stakeholders and leadership

AI Engineer (Python, GenAI/LLMs + ML Fundamentals)

REQUIRED QUALIFICATIONS

• 3+ years of hands-on experience as an AI/ML Engineer or Applied Scientist, with proven production deployments including at least one LLM-based or agentic system taken to production.
• Strong Python skills and solid software engineering fundamentals (version control, testing, design patterns, code reviews).
• Deep Learning & NLP: Strong grasp of transformer architectures, attention, tokenization, embeddings, and modern NLP techniques. Hands-on with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).
• Agentic & RAG stack: Working knowledge of frameworks such as LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen, plus vector stores (Pinecone, Weaviate, Qdrant, pgvector, or FAISS) and reranking strategies.
• Serving & optimization: Experience with inference servers such as vLLM, TGI, or Triton, and familiarity with quantization (GPTQ, AWQ, GGUF).
• MLOps & infra: Hands-on with tools like MLflow, Weights & Biases, Airflow, or Kubeflow; comfortable with Docker, Kubernetes, GPU workloads, and at least one major cloud (AWS / Azure / GCP).
• Soft skills: High bias for action, strong communication, ownership mindset, and intellectual curiosity.
• Nice to have: Open-source contributions, multimodal model experience, on-device SLM deployment, or familiarity with LLM security (OWASP LLM Top 10).

EDUCATION

• B.Tech or M.Tech in Computer Science, Data Science Engineering, AI/ML Engineering, or a closely related quantitative discipline.
• Equivalent practical experience supported by a strong portfolio (open-source work, publications, or production deployments) will also be considered.

SOFT SKILLS

• Strong problem-solving and ownership mindset; comfortable operating in ambiguity.
• Clear communication of technical trade-offs and experiment results to stakeholders.
• Collaborative approach with engineering, product, and data teams.

Solutions By Text is committed to promoting the values of diversity and inclusion throughout the business. Whether it is through recruitment, retention, career progression or training and development, we are committed to improving opportunities for people regardless of their background or circumstances.