What are the responsibilities and job description for the Senior ML Engineer – Gen AI - Nearshore position at Alpha Business Solutions LLC?
PURPOSE:
At ***, we’re building AI-powered search and agentic experiences that help guests, customers, and internal teams find information, complete tasks, and make better decisions faster. This role would focus on designing, building, and deploying enterprise search, retrieval, and data agent solutions that connect large-scale data assets with practical business workflows. Like the more senior GenAI engineering role, it would sit at the intersection of ML engineering, MLOps, data engineering, and product delivery, but with less emphasis on people management and organizational leadership and more emphasis on hands-on technical execution.
POSITION RESPONSIBILITIES:
• Design, build, and deploy enterprise search and information retrieval systems across structured and unstructured data sources.
• Develop data agents and AI-powered workflows that can reason over enterprise knowledge, retrieve relevant context, and support downstream user actions.
• Build and maintain real-time and batch data pipelines that power search indexing, retrieval, ranking, and agent orchestration.
• Partner closely with data scientists, product owners, architects, and data engineers to deliver end-to-end AI products.
• Contribute to scalable ML/AI infrastructure using AWS-native services and MLOps best practices including CI/CD, monitoring, reproducibility, governance, and observability.
• Help evaluate and improve search relevance, retrieval quality, latency, reliability, and responsible AI guardrail s in production environments
• Flexible and adaptable to learning and understanding new technologies
• Highly self-motivated and directed
• Demonstrate a commitment to *** core values
EXPERIENCE AND QUALIFICATIONS:
You are a hands-on ML engineer with experience building production AI/ML systems and a strong interest in search, retrieval, LLM-powered applications, and agentic workflows. You are comfortable working across Python, SQL, data pipelines, cloud infrastructure, and model deployment, and you thrive in collaborative environments where business impact matters. This mirrors the current role’s focus on enterprise-grade AI/ML platforms, experimentation, and business alignment, but removes the management expectations.
EDUCATION:
• Ph.D. or Master’s degree in computer science, Machine Learning, Software Engineering, or a related field
• 5-6 years of experience in machine learning, search, information retrieval, MLOps, or applied AI engineering.
• 3-4 years of experience in Gen AI systems especially relevant applications of decoder LLMs in agentic implementations in the last 2 years
• Experience with NLP/NLU, retrieval systems, vector search, ranking, or LLM-based applications in production.
• Strong programming skills in Python, plus experience with SQL, PySpark, APIs, and containerization such as Docker
• Experience in frameworks such as LangChain, LangGraph and fast inference
implementations like vLLM, SGLang, TensorRT-LLM, ONNX Runtime etc.
• Familiarity with AWS services such as SageMaker, Lambda, ECS/EKS, Step Functions, or Glue.
• Experience building scalable data pipelines and production systems for low-latency and batch workloads.
At ***, we’re building AI-powered search and agentic experiences that help guests, customers, and internal teams find information, complete tasks, and make better decisions faster. This role would focus on designing, building, and deploying enterprise search, retrieval, and data agent solutions that connect large-scale data assets with practical business workflows. Like the more senior GenAI engineering role, it would sit at the intersection of ML engineering, MLOps, data engineering, and product delivery, but with less emphasis on people management and organizational leadership and more emphasis on hands-on technical execution.
POSITION RESPONSIBILITIES:
• Design, build, and deploy enterprise search and information retrieval systems across structured and unstructured data sources.
• Develop data agents and AI-powered workflows that can reason over enterprise knowledge, retrieve relevant context, and support downstream user actions.
• Build and maintain real-time and batch data pipelines that power search indexing, retrieval, ranking, and agent orchestration.
• Partner closely with data scientists, product owners, architects, and data engineers to deliver end-to-end AI products.
• Contribute to scalable ML/AI infrastructure using AWS-native services and MLOps best practices including CI/CD, monitoring, reproducibility, governance, and observability.
• Help evaluate and improve search relevance, retrieval quality, latency, reliability, and responsible AI guardrail s in production environments
• Flexible and adaptable to learning and understanding new technologies
• Highly self-motivated and directed
• Demonstrate a commitment to *** core values
EXPERIENCE AND QUALIFICATIONS:
You are a hands-on ML engineer with experience building production AI/ML systems and a strong interest in search, retrieval, LLM-powered applications, and agentic workflows. You are comfortable working across Python, SQL, data pipelines, cloud infrastructure, and model deployment, and you thrive in collaborative environments where business impact matters. This mirrors the current role’s focus on enterprise-grade AI/ML platforms, experimentation, and business alignment, but removes the management expectations.
EDUCATION:
• Ph.D. or Master’s degree in computer science, Machine Learning, Software Engineering, or a related field
• 5-6 years of experience in machine learning, search, information retrieval, MLOps, or applied AI engineering.
• 3-4 years of experience in Gen AI systems especially relevant applications of decoder LLMs in agentic implementations in the last 2 years
• Experience with NLP/NLU, retrieval systems, vector search, ranking, or LLM-based applications in production.
• Strong programming skills in Python, plus experience with SQL, PySpark, APIs, and containerization such as Docker
• Experience in frameworks such as LangChain, LangGraph and fast inference
implementations like vLLM, SGLang, TensorRT-LLM, ONNX Runtime etc.
• Familiarity with AWS services such as SageMaker, Lambda, ECS/EKS, Step Functions, or Glue.
• Experience building scalable data pipelines and production systems for low-latency and batch workloads.