onsite

Senior AI/ML Engineer - NLP & Generative AI

MyCareernet is seeking a Senior AI/ML Engineer with expertise in NLP and Generative AI. This role involves designing and developing advanced AI solutions, building RAG pipelines, and deploying Large Language Models for various use cases. The engineer will collaborate with cross-functional teams to create scalable, intelligent applications.

About the role

Roles and Responsibilities

Design, develop, and optimize NLP-driven AI solutions using advanced models and techniques (NER, embeddings, summarization).
Build and productionize Retrieval-Augmented Generation (RAG) pipelines and agentic workflows for intelligent, context-aware applications.
Fine-tune, prompt-engineer, and deploy Large Language Models (LLMs) such as OpenAI, Anthropic, Falcon, and LLaMA for domain-specific use cases.
Collaborate with data scientists, backend engineers, and cloud architects to develop scalable AI-first systems.
Evaluate and integrate third-party APIs, open-source frameworks, and models for generative AI use cases.
Continuously monitor and improve model accuracy, performance, and latency in production settings.
Implement observability, explainability, and monitoring tools to ensure robust AI model performance.
Ensure all deployed solutions adhere to enterprise standards for reliability, traceability, and maintainability.

Skills Required

Strong expertise in NLP techniques: NER, embeddings, summarization, sentiment analysis, topic modeling.
Hands-on experience with LLMs (e.g., OpenAI, Anthropic, Falcon, LLaMA, Mistral).
Proficiency in Python and ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
Experience with Generative AI workflows: fine-tuning, prompt engineering, RAG pipelines, and agent-based systems.
Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus).
Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Strong understanding of MLOps practices: monitoring, observability, CI/CD for ML models.
Excellent problem-solving, collaboration, and communication skills.

Education

Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or a related field.

About the role

Roles and Responsibilities

Design, develop, and optimize NLP-driven AI solutions using advanced models and techniques (NER, embeddings, summarization).
Build and productionize Retrieval-Augmented Generation (RAG) pipelines and agentic workflows for intelligent, context-aware applications.
Fine-tune, prompt-engineer, and deploy Large Language Models (LLMs) such as OpenAI, Anthropic, Falcon, and LLaMA for domain-specific use cases.
Collaborate with data scientists, backend engineers, and cloud architects to develop scalable AI-first systems.
Evaluate and integrate third-party APIs, open-source frameworks, and models for generative AI use cases.
Continuously monitor and improve model accuracy, performance, and latency in production settings.
Implement observability, explainability, and monitoring tools to ensure robust AI model performance.
Ensure all deployed solutions adhere to enterprise standards for reliability, traceability, and maintainability.

Skills Required

Strong expertise in NLP techniques: NER, embeddings, summarization, sentiment analysis, topic modeling.
Hands-on experience with LLMs (e.g., OpenAI, Anthropic, Falcon, LLaMA, Mistral).
Proficiency in Python and ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
Experience with Generative AI workflows: fine-tuning, prompt engineering, RAG pipelines, and agent-based systems.
Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus).
Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Strong understanding of MLOps practices: monitoring, observability, CI/CD for ML models.
Excellent problem-solving, collaboration, and communication skills.

Education

Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or a related field.