Senior AI/ML Engineer

Senior AI/ML Engineer – Full-Time

Location:

United States

Employment Type:

Full-Time

Experience:

10+ Years

Work Authorization:

US Work Authorization Required

Job Summary

We are seeking an experienced

Senior AI/ML Engineer

with 10+ years of experience designing, developing, and deploying scalable machine learning and AI solutions. The ideal candidate will have strong expertise in

Python, machine learning, deep learning, Generative AI, LLMs, NLP, cloud platforms, MLOps, and production-grade AI systems

.

Key Responsibilities

• Design, develop, and productionize

machine learning and AI solutions

for complex business problems.

• Build and optimize

ML models, deep learning architectures, NLP solutions, and Generative AI applications

.

• Develop

LLM-powered applications

using techniques such as prompt engineering,

RAG, embeddings, vector search, fine-tuning, and evaluation

.

• Work with

OpenAI, Azure OpenAI, AWS Bedrock, Hugging Face, LangChain, and LlamaIndex

or comparable AI platforms.

• Develop scalable data and feature pipelines using

Python, SQL, Spark, and PySpark

.

• Implement and optimize models using

TensorFlow, PyTorch, Scikit-learn, XGBoost

, and other ML frameworks.

• Deploy AI/ML models using

AWS, Azure, or GCP

cloud services.

• Build

MLOps pipelines

for model training, validation, deployment, monitoring, versioning, and retraining.

• Implement model serving using

Docker, Kubernetes, REST APIs, FastAPI

, and cloud-native technologies.

• Work with

MLflow, Kubeflow, Azure ML, SageMaker

, or equivalent MLOps platforms.

• Develop AI solutions involving

NLP, recommendation systems, classification, forecasting, anomaly detection, and predictive analytics

.

• Implement

vector databases

such as Pinecone, FAISS, Weaviate, Milvus, or Azure AI Search.

• Establish model evaluation, observability, performance monitoring, and responsible AI practices.
• Collaborate with data engineers, software engineers, architects, product teams, and business stakeholders.
• Mentor junior and mid-level engineers and provide technical leadership across AI/ML initiatives.

Required Technical Skills

• 10+ years

of experience in AI/ML, machine learning engineering, data science, or related fields.

• Strong programming experience with

Python

and

SQL

.

• Strong knowledge of

Machine Learning, Deep Learning, NLP, and Generative AI

.

• Hands-on experience with

LLMs, RAG, prompt engineering, embeddings, vector databases, and model evaluation

.

• Experience with

PyTorch, TensorFlow, Scikit-learn, XGBoost

or similar frameworks.

• Strong understanding of

Transformers, neural networks, model training, fine-tuning, and inference optimization

.

• Experience with at least one major cloud platform:

AWS, Azure, or GCP

.

• Strong knowledge of

MLOps, CI/CD, model lifecycle management, and production deployment

.

• Experience with

Docker, Kubernetes, Git, REST APIs, and microservices

.

• Experience with

Spark/PySpark

and large-scale data processing.

• Strong understanding of

data structures, algorithms, statistics, probability, and model evaluation techniques

.

Preferred Skills

• Experience with

Azure OpenAI, AWS Bedrock, Amazon SageMaker, Azure Machine Learning, or Vertex AI

.

• Experience building

Agentic AI / AI Agent

solutions.

• Knowledge of

LangChain, LlamaIndex, Semantic Kernel

, or comparable frameworks.

• Experience with

RAG architecture, hybrid search, reranking, and knowledge graphs

.

• Experience with

LLM fine-tuning, LoRA/PEFT, quantization, and inference optimization

.

• Knowledge of

Kafka, Airflow, Databricks, Snowflake, or Delta Lake

.

• Experience implementing

AI governance, security, privacy, explainability, and responsible AI

.

• Strong communication, architecture, problem-solving, and technical leadership skills.

Education

Bachelor’s or Master’s degree in

Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics

, or a related technical discipline.

Ideal Candidate Profile

The ideal candidate should be capable of taking an AI/ML initiative from

problem definition → data preparation → model development → experimentation → deployment → monitoring → continuous optimization

, while working closely with engineering and business teams in a production environment.