45 Sr AI Ml Engineer Nlp Llms Rag Systems jobs in Ajit

Job Title:

AI Engineer – LLMs & Agentive AI (Amazon Bedrock Focus)

Job Summary:

We are seeking a skilled and forward-thinking AI Engineer to join our team in building intelligent, context-aware AI applications using LLMs, Amazon Bedrock AI Agents, and agentic architectures. You will design, develop, and deploy multi-agent systems, autonomous workflows, and intelligent task handlers leveraging foundation models (FMs) and orchestration tools to deliver scalable and secure solutions.

Key Responsibilities:

LLM Development & Integration

Design, fine-tune, and integrate large language models (LLMs) using platforms such as Amazon Bedrock, OpenAI, or Anthropic.

Leverage foundation models (Jurassic, Claude, Titan, etc.) via Bedrock to build secure, enterprise-grade generative AI applications.

Apply prompt engineering techniques and prompt chaining for dynamic, multi-turn interactions.

AI Agent Development

Build agentic AI systems using Amazon Bedrock Agents or similar frameworks (LangChain, ReAct, AutoGPT, CrewAI, etc.).

Define tools, actions, and memory systems for agents to perform tasks autonomously based on goals, context, and user feedback.

Implement custom logic for orchestration, context management, and multi-agent collaboration.

System Integration & Infrastructure

Develop scalable, production-grade pipelines for serving AI agents via APIs or UI-based interfaces.

Integrate vector databases (e.g., Pinecone, Weaviate, FAISS) for semantic search and retrieval-augmented generation (RAG).

Use AWS services (Lambda, S3, API Gateway, DynamoDB, CloudWatch) to build secure and scalable agent pipelines.

Required Skills & Experience:

4–7 years in AI/ML engineering, with deep understanding of LLMs and transformer-based architectures.

Hands-on experience with Amazon Bedrock, including working with FMs and Bedrock Agents.

Proficiency in Python and experience with LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

Strong understanding of prompt engineering, RAG pipelines, and tool/function calling for agents.

Familiarity with deploying applications on AWS cloud infrastructure using serverless or container-based patterns.

We’re seeking a hands-on AI/ML Engineer having 3-6 years of work experience with deep expertise in large language models, retrieval-augmented generation (RAG), and cloud-native ML development on AWS. You'll be a key driver in building scalable, intelligent learning systems powered by cutting-edge AI and robust AWS infrastructure.

Core Skills & Technologies:

LLM Ecosystem & APIs

• OpenAI, Anthropic, Cohere
• Hugging Face Transformers
• LangChain, LlamaIndex (RAG orchestration)

Vector Databases & Indexing

• FAISS, Pinecone, Weaviate

AWS-Native & ML Tooling

• Amazon SageMaker (training, deployment, pipelines)
• AWS Lambda (event-driven workflows)
• Amazon Bedrock (foundation model access)
• Amazon S3 (data lakes, model storage)
• AWS Step Functions (workflow orchestration)
• AWS API Gateway & IAM (secure ML endpoints)
• CloudWatch, Athena, DynamoDB (monitoring, analytics, structured storage)

Languages & ML Frameworks

• Python (primary), PyTorch, TensorFlow
• NLP, RAG systems, embeddings, prompt engineering

What You’ll Do

• Model Development & Tuning
• Fine-tune and deploy LLMs and custom models using AWS SageMaker
• Build RAG pipelines with LlamaIndex/LangChain and vector search engines
• Scalable AI Infrastructure
• Architect distributed model training and inference pipelines on AWS
• Design secure, efficient ML APIs with Lambda, API Gateway, and IAM
• Product Integration
• Embed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpoints
• Rapid Experimentation
• Prototype multimodal and few-shot learning workflows using AWS services
• Automate experimentation and A/B testing with Step Functions and SageMaker Pipelines
• Data & Impact Analysis
• Leverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performance
• Cross-Team Collaboration
• Work closely with educators, designers, and engineers to deliver AI features that enhance student learning

Who You Are:

• Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMs
• AWS-Fluent: Extensive experience with AWS ML services (especially SageMaker, Lambda, and Bedrock)
• Product-Minded: You care about user experience and turning ML into real-world value
• Startup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hats
• Mission-Aligned: Passionate about education, human learning, and AI for good

Bonus Points:

• Hands-on experience fine-tuning LLMs or building agentic systems using AWS
• Open-source contributions in AI/ML or NLP communities
• Familiarity with AWS security best practices (IAM, VPC, private endpoints)

We’re seeking a hands-on AI/ML Engineer with deep expertise in large language models, retrieval-augmented generation (RAG), and cloud-native ML development on AWS. You'll be a key driver in building scalable, intelligent learning systems powered by cutting-edge AI and robust AWS infrastructure.

If you’re passionate about combining NLP, deep learning, and real-world application at scale—this is the role for you. 6-10 years of specialized experience in AI/ML is required.

Core Skills & Technologies

LLM Ecosystem & APIs

• OpenAI, Anthropic, Cohere
• Hugging Face Transformers
• LangChain, LlamaIndex (RAG orchestration)

Vector Databases & Indexing

• FAISS, Pinecone, Weaviate

AWS-Native & ML Tooling

• Amazon SageMaker (training, deployment, pipelines)
• AWS Lambda (event-driven workflows)
• Amazon Bedrock (foundation model access)
• Amazon S3 (data lakes, model storage)
• AWS Step Functions (workflow orchestration)
• AWS API Gateway & IAM (secure ML endpoints)
• CloudWatch, Athena, DynamoDB (monitoring, analytics, structured storage)

Languages & ML Frameworks

• Python (primary), PyTorch, TensorFlow
• NLP, RAG systems, embeddings, prompt engineering

What You’ll Do

• Model Development & Tuning
• Designs architecture for complex AI systems and makes strategic technical decisions
• Evaluates and selects appropriate frameworks, techniques, and approaches
• Fine-tune and deploy LLMs and custom models using AWS SageMaker
• Build RAG pipelines with LlamaIndex/LangChain and vector search engines
• Scalable AI Infrastructure
• Architect distributed model training and inference pipelines on AWS
• Design secure, efficient ML APIs with Lambda, API Gateway, and IAM
• Product Integration
• Leads development of novel solutions to challenging problems
• Embed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpoints
• Rapid Experimentation
• Prototype multimodal and few-shot learning workflows using AWS services
• Automate experimentation and A/B testing with Step Functions and SageMaker Pipelines
• Data & Impact Analysis
• Leverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performance
• Cross-Team Collaboration
• Work closely with educators, designers, and engineers to deliver AI features that enhance student learning
• Mentors junior engineers and provides technical leadership

Who You Are

• Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMs
• AWS-Fluent: Extensive experience with AWS ML services (especially SageMaker, Lambda, and Bedrock)
• Product-Minded: You care about user experience and turning ML into real-world value
• Startup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hats
• Mission-Aligned: Passionate about education, human learning, and AI for good

Bonus Points

• Hands-on experience fine-tuning LLMs or building agentic systems using AWS
• Open-source contributions in AI/ML or NLP communities

Familiarity with AWS security best practices (IAM, VPC, private endpoints)

Job Title: NLP/Audio ML Engineer – ASR, TTS & Transformers

Location: Remote

Employment Type: Full-Time

About Us:

Join a cutting-edge AI company pushing the boundaries of speech and language processing. We’re developing scalable, production-ready solutions in Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and advanced Transformer-based NLP. If you’re passionate about deep learning, voice tech, and building state-of-the-art systems, we want to hear from you.

Key Responsibilities:

• esign and develop sequence-to-sequence models with attention and Transformer architectures

• B ild and fine-tune encoder–decoder frameworks using TensorFlow Keras or PyTorch

• Work with state-of-the-art ASR/TTS toolkits (e.g. ESPnet, Fairseq S2T, Tacotron, WaveNet)

• I plement audio feature extraction pipelines (e.g. MFCCs, spectrograms, mel-filterbanks)

• C llect, clean, and align large-scale parallel corpora (text and/or speech)

• E aluate models using metrics like BLEU, TER, MOS, and word-error-rate

• O timize and deploy models via Docker/Kubernetes, serving over REST or gRPC

• A ply model compression techniques (quantization, pruning) for efficient inference

• C stomize Transformer models (BART, T5) with advanced techniques like novel attention heads and positional encodings

What You Bring:

• S rong knowledge of deep learning for NLP and speech

• H nds-on experience with large-scale model training and deployment

• F miliarity with cloud-based ML environments and scalable infrastructure

• A collaborative, solutions-oriented mindset

Bonus:

• C ntributions to open-source projects or publications in ASR/NLP/TTS

• E perience in real-time inference and streaming architectures

About the Job

As an AI/ML Engineer, you will be responsible for designing, validating, and integrating cutting-edge machine learning models and algorithms. Collaborate closely with cross-functional teams, including data scientists, to recognize and establish project objectives. Oversee data infrastructure maintenance, ensuring streamlined and scalable data operations. Stay updated with advancements in AI and propose their integration for operational enhancement. Effectively convey detailed data insights to non-technical stakeholders. Uphold stringent data privacy and security protocols. Engage in the full lifecycle of AI projects, spanning from ideation through deployment and continuous upkeep.

Core Responsibilities

• Develop, validate, and implement machine learning models and algorithms.
• Collaborate with data scientists and other stakeholders to understand and define project goals.
• Maintain data infrastructure and ensure scalability and efficiency of data-related operations.
• Stay abreast of the latest developments in the field of AI/Client and recommend ways to implement them in our operations.
• Communicate complex data findings in a clear and understandable manner to non-technical stakeholders.
• Adhere to data privacy and security guidelines.
• Participate in the entire AI project lifecycle, from concept to deployment and maintenance.

Required Skills

• Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals.
• Solid theoretical understanding of machine learning.
• Experience with mapping NLP models (BERT and GPT) to accelerators and awareness of trade-offs across memory, bandwidth, and compute.
• Experience with Vector databases like Chroma Db, Pinecone, PGVector or similar
• Experience with Large language Models like GPT 3.5, GPT 4, GPT 4.o, Llama, Gemini, Mistral etc.
• Experience in LLM integration framework like langchain, Llamaindex, AgentGPT etc
• Experience with ML Models from definition to deployment, including training, quantization, sparsity, model preprocessing, and deployment.
• Proficiency in Python development in a Linux environment and using standard development tools.
• Experience with deep learning frameworks (such as PyTorch, Tensorflow, Keras, Spark).
• Working knowledge of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
• Experience in training, tuning, and deploying ML models for Computer Vision (e.g., ResNet), and/or Recommendation Systems (e.g., DLRM).
• Experience deploying ML workloads on distributed systems.
• Self-motivated team player with a strong sense of ownership and leadership.
• Strong verbal, written, and organizational skills for effective communication and documentation.
• Research background with a publication record.
• Work experience at a cloud provider or AI compute/sub-system company.
• Knowledge of cloud computing platforms and services, such as AWS, Azure, or Google Cloud.
• Experience with information security and secure development best practices.

Qualifications

• Bachelor's or higher degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field.
• 2-4 yrs of hands-on experience in AI/ML

Role :
:
Senior Gen AI Engineer – GenAI / ML (Python, Langchain)

Location :
:
Remote/India

Duration :
:
Full Time

Overlap:
4hrs overlap with PST timezone

Focus:
Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration.

Key Responsibilities:

- Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines.

- Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions.

- Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives.

- Translate business needs into scalable, production-ready AI solutions.

- Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases.

- Contribute to architecture discussions, code reviews, and performance optimization.

Skills Required:

- Proficient in Python, Langchain, and SQL.

- Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows.

- Background in machine learning or software engineering with a focus on system-level thinking.

- Experience working with cloud platforms like AWS, Azure, or GCP.

- Ability to work independently while collaborating effectively across teams.

- Excellent communication and stakeholder management skills.

Preferred Qualifications:

- 1+ years of hands-on experience in LLMs and Generative AI techniques.

- Experience contributing to ML/AI product pipelines or end-to-end deployments.

- Familiarity with MLOps and scalable deployment patterns for AI models.

- Prior exposure to client-facing projects or cross-functional AI teams.

Job Title:
NLP/Audio ML Engineer – ASR, TTS & Transformers

Location:
Remote

Employment Type:
Full-Time

About Us:

Join a cutting-edge AI company pushing the boundaries of speech and language processing. We’re developing scalable, production-ready solutions in Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and advanced Transformer-based NLP. If you’re passionate about deep learning, voice tech, and building state-of-the-art systems, we want to hear from you.

Key Responsibilities:

- Design and develop sequence-to-sequence models with attention and Transformer architectures

- Build and fine-tune encoder–decoder frameworks using TensorFlow Keras or PyTorch

- Work with state-of-the-art ASR/TTS toolkits (e.G. ESPnet, Fairseq S2T, Tacotron, WaveNet)

- Implement audio feature extraction pipelines (e.G. MFCCs, spectrograms, mel-filterbanks)

- Collect, clean, and align large-scale parallel corpora (text and/or speech)

- Evaluate models using metrics like BLEU, TER, MOS, and word-error-rate

- Optimize and deploy models via Docker/Kubernetes, serving over REST or gRPC

- Apply model compression techniques (quantization, pruning) for efficient inference

- Customize Transformer models (BART, T5) with advanced techniques like novel attention heads and positional encodings

What You Bring:

- Strong knowledge of deep learning for NLP and speech

- Hands-on experience with large-scale model training and deployment

- Familiarity with cloud-based ML environments and scalable infrastructure

- A collaborative, solutions-oriented mindset

Bonus:

- Contributions to open-source projects or publications in ASR/NLP/TTS

- Experience in real-time inference and streaming architectures