Apexon
About the Role
Apexon is seeking an experienced
AI/ML Engineer
with strong expertise in
LLM development, MLOps, and building scalable GenAI solutions . You will design, build, and operationalize AI/ML systems that support enterprise clients across healthcare, BFSI, retail, and digital transformation engagements. The ideal candidate has hands-on experience building
end-to-end machine learning pipelines , optimizing
large language model workflows , and deploying
secure ML systems
in production environments.
Responsibilities LLM & AI Solution Development Build, fine-tune, evaluate, and optimize
Large Language Models (LLMs)
for client-specific use cases such as document intelligence, chatbot automation, code generation, and workflow orchestration. Develop RAG (Retrieval-Augmented Generation) pipelines using enterprise knowledge bases. Implement prompt engineering, guardrails, hallucination reduction strategies, and safety frameworks. Work with transformer-based architectures (GPT, LLaMA, Mistral, Falcon, etc.) and develop optimized model variants for low-latency and cost-efficient inference. Machine Learning Engineering Develop scalable ML systems including feature pipelines, training jobs, and batch/real-time inference services. Build and automate training, validation, and monitoring workflows for predictive and GenAI models. Perform offline evaluation, A/B testing, performance benchmarking, and business KPI tracking. MLOps & Platform Engineering Build and maintain end-to-end MLOps pipelines using: AWS SageMaker, Databricks, MLflow, Kubernetes, Docker, Terraform, Airflow Manage CICD pipelines for model deployment, versioning, reproducibility, and governance. Implement enterprise-grade
model monitoring
(data drift, performance, cost, safety). Maintain infrastructure for vector stores, embeddings pipelines, feature stores, and inference endpoints. Data Engineering & Infrastructure Build data pipelines for structured and unstructured data using: Snowflake, S3, Kafka, Delta Lake, Spark (PySpark) Work on data ingestion, transformation, quality checks, cataloging, and secure storage. Ensure all systems adhere to Apexon and client-specific security, IAM, and compliance standards. Cross-Functional Collaboration Partner with product managers, data engineers, cloud architects, and QA teams. Translate business requirements into scalable AI/ML solutions. Ensure model explainability, governance documentation, and compliance adherence.
Basic Qualifications Bachelors or Masters degree in Computer Science, Engineering, AI/ML, Data Science, or related field. 4+ years of experience in
AI/ML engineering , including
1+ years working with LLMs/GenAI . Strong experience with
Python ,
Transformers ,
PyTorch/TensorFlow , and NLP frameworks. Hands-on expertise with MLOps platforms:
SageMaker, MLflow, Databricks, Kubernetes, Docker . Strong SQL and data engineering experience (Snowflake, S3, Spark, Kafka).
Preferred Qualifications Experience implementing Generative AI solutions for enterprise clients. Expertise in distributed training, quantization, optimization, and GPU acceleration. Experience with: Vector Databases (Pinecone, Weaviate, FAISS) RAG frameworks (LangChain, LlamaIndex) Monitoring tools (Prometheus, Grafana, CloudWatch) Understanding of model governance, fairness evaluation, and client compliance frameworks.
AI/ML Engineer
with strong expertise in
LLM development, MLOps, and building scalable GenAI solutions . You will design, build, and operationalize AI/ML systems that support enterprise clients across healthcare, BFSI, retail, and digital transformation engagements. The ideal candidate has hands-on experience building
end-to-end machine learning pipelines , optimizing
large language model workflows , and deploying
secure ML systems
in production environments.
Responsibilities LLM & AI Solution Development Build, fine-tune, evaluate, and optimize
Large Language Models (LLMs)
for client-specific use cases such as document intelligence, chatbot automation, code generation, and workflow orchestration. Develop RAG (Retrieval-Augmented Generation) pipelines using enterprise knowledge bases. Implement prompt engineering, guardrails, hallucination reduction strategies, and safety frameworks. Work with transformer-based architectures (GPT, LLaMA, Mistral, Falcon, etc.) and develop optimized model variants for low-latency and cost-efficient inference. Machine Learning Engineering Develop scalable ML systems including feature pipelines, training jobs, and batch/real-time inference services. Build and automate training, validation, and monitoring workflows for predictive and GenAI models. Perform offline evaluation, A/B testing, performance benchmarking, and business KPI tracking. MLOps & Platform Engineering Build and maintain end-to-end MLOps pipelines using: AWS SageMaker, Databricks, MLflow, Kubernetes, Docker, Terraform, Airflow Manage CICD pipelines for model deployment, versioning, reproducibility, and governance. Implement enterprise-grade
model monitoring
(data drift, performance, cost, safety). Maintain infrastructure for vector stores, embeddings pipelines, feature stores, and inference endpoints. Data Engineering & Infrastructure Build data pipelines for structured and unstructured data using: Snowflake, S3, Kafka, Delta Lake, Spark (PySpark) Work on data ingestion, transformation, quality checks, cataloging, and secure storage. Ensure all systems adhere to Apexon and client-specific security, IAM, and compliance standards. Cross-Functional Collaboration Partner with product managers, data engineers, cloud architects, and QA teams. Translate business requirements into scalable AI/ML solutions. Ensure model explainability, governance documentation, and compliance adherence.
Basic Qualifications Bachelors or Masters degree in Computer Science, Engineering, AI/ML, Data Science, or related field. 4+ years of experience in
AI/ML engineering , including
1+ years working with LLMs/GenAI . Strong experience with
Python ,
Transformers ,
PyTorch/TensorFlow , and NLP frameworks. Hands-on expertise with MLOps platforms:
SageMaker, MLflow, Databricks, Kubernetes, Docker . Strong SQL and data engineering experience (Snowflake, S3, Spark, Kafka).
Preferred Qualifications Experience implementing Generative AI solutions for enterprise clients. Expertise in distributed training, quantization, optimization, and GPU acceleration. Experience with: Vector Databases (Pinecone, Weaviate, FAISS) RAG frameworks (LangChain, LlamaIndex) Monitoring tools (Prometheus, Grafana, CloudWatch) Understanding of model governance, fairness evaluation, and client compliance frameworks.