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

Solutions By Text; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Solutions By Text, Bengaluru, is seeking an AI Engineer to join the Labs team. You will own the full ML lifecycle for predictive and generative AI systems, from data curation to deployment and monitoring, with hands-on development across research and production.

You will build scalable MLOps pipelines, optimize inference, and apply state-of-the-art LLM/agentic techniques (LoRA/QLoRA, RLHF/DPO). A bias for action and collaboration with cross-functional teams is essential.

Qualifications

• 3+ years of hands-on experience as an AI/ML Engineer or Applied Scientist with production deployments.
• Strong Python skills and solid software engineering fundamentals (version control, testing, design patterns, code reviews).
• Deep Learning & NLP: transformer architectures, embeddings, tokenization; PyTorch & Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).
• Agentic & RAG stack: LangChain/LangGraph/LlamaIndex, vector stores, retrieval & reranking.
• Serving & optimization: inference servers (vLLM/TGI/Triton) and quantization.
• MLOps & infra: MLflow, Weights & Biases, Airflow, Kubeflow; Docker, Kubernetes; cloud experience (AWS/GCP/Azure).
• Soft skills: action bias, clear communication, ownership mindset, curiosity.

Responsibilities

• End-to-end ML ownership: data curation, model building, evaluation, deployment, monitoring, retraining for predictive and generative AI systems.
• Production-grade MLOps: scalable training pipelines, CI/CD, model registry, A/B testing, drift detection, automated retraining; optimize latency/cost.
• LLMs and SLMs: fine-tune and deploy models using LoRA/QLoRA, PEFT, instruction tuning, RLHF/DPO, quantization/distillation as needed.
• Agentic systems: design/productionize RAG pipelines, tool calling, memory, planning loops, multi-agent orchestration with guardrails.
• Quality and trust: evaluation frameworks (offline/online), LLM-as-judge, red-teaming; mitigate hallucinations and bias.
• Rapid innovation: track SOTA research, prototype quickly, demos and tech talks for stakeholders.

Skills

Python

Deep Learning

NLP

AI Research

Problem solving

Ownership

Tools

LangChain

LangGraph

LlamaIndex

Pinecone

FAISS

Docker

Kubernetes

Job description

Position Summary

We are hiring an AI Engineer for our Labs team a fast-moving group that prototypes emerging AI capabilities and takes the most promising ones to production at scale. You will operate at the intersection of research and engineering: turning new papers and ideas into working demos within days, then hardening them into reliable, production-grade systems. The ideal candidate stays on the frontier of AI, is hands-on with the full ML lifecycle, and thrives in ambiguity with a strong bias for action.

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.

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).

PREFERRED / NICE TO HAVE

• Agentic architectures (tool use, multi-step workflows, orchestration patterns).
• MLOps experience: model/version management, deployment automation, monitoring, drift/quality tracking, and rollback strategies.
• Experience with vector databases / search systems (semantic + keyword/hybrid retrieval).
• Cloud experience (AWS/GCP/Azure) and infrastructure fundamentals (IAM, secrets management, networking basics).
• Exposure to fine-tuning / adaptation techniques for smaller models where applicable.

SOFT SKILLS

• Strong problem-solving and ownership mindset; comfortable operating in ambiguity.
• Clear communication of technical tradeoffs 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.