Gen AI Engineer

Role
Overview

You
will be involved in full lifecycle AI solution delivery – from research and
prototyping to scaling and deployment. The ideal candidate combines strong
theoretical ML/DL grounding with applied experience in foundation models, LLM
fine-tuning, vector search, Gen AI application frameworks, and deployment on
large-scale infrastructure.

Key
Responsibilities

·       Architect, build, and deploy Generative AI solutions
tailored to business problems.

·       Fine-tune and customize foundation/LLM models (e.g.,
GPT, LLaMA, Mistral, Falcon, Claude, Gemma).

·       Build LLM-powered applications using frameworks such
as LangChain, Haystack, LlamaIndex.

·       Develop and manage RAG (Retrieval Augmented
Generation) pipelines integrating vector databases (FAISS, Pinecone, Weaviate,
Milvus, ChromaDB).

·       Work with cloud AI services such as Azure OpenAI, AWS
Bedrock, SageMaker, GCP Vertex AI.

·       Implement MLOps pipelines for model
training/monitoring using tools like MLflow, Kubeflow, Weights & Biases,
DVC.

·       Leverage Hugging Face ecosystem (Transformers,
Diffusers, PEFT, Datasets) for model experimentation.

·       Optimize AI workflows with GPU acceleration and
inference optimization (e.g., ONNX, TensorRT, DeepSpeed, vLLM).

·       Design and enforce secure, ethical, and responsible AI
practices in all deployments.

·       Collaborate with consultants, data engineers, and
business analysts to understand client problems and deliver measurable
solutions.

·       Mentor junior engineers; contribute to internal
accelerators and reusable solution templates.

Requirements

Required
Qualifications

·       Education: Bachelor’s/Master’s in Computer Science,
Data Science, AI/ML, or a related field.

·       Experience: 5+ years in ML/DL, with minimum 2 years in
Generative AI solution development.

·       Expertise with Python and ML/DL libraries (PyTorch,
TensorFlow, JAX).

·       Strong knowledge of LLM training/fine-tuning
techniques: LoRA, QLoRA, PEFT, instruction tuning.

·       Proficiency in prompt engineering and evaluation of
model outputs.

·       Hands-on with vector databases and indexing pipelines
for semantic search.

·       Familiar with containerization and deployment tools
(Docker, Kubernetes, Helm).

·       Exposure to CI/CD pipelines for AI solutions and
cloud-native patterns.

Preferred
Skills

·       Experience in enterprise AI implementation (chatbots,
document intelligence, knowledge assistants, customer interaction systems).

·       Exposure to multimodal AI (e.g., CLIP, Stable
Diffusion, DALL·E, Whisper).

·       Contributions to open-source or personal projects
showcasing Gen AI apps.

·       Experience in reinforcement learning (RLHF/DPO) for
model alignment.

·       Working knowledge of streaming and event-driven
architectures (Kafka, Flink) for real-time AI applications.

Benefits