AI/ML Engineer
Actively Hiring
Full-time Posted 8 months ago
Role overview
- check_circle 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.
- check_circle Develop RAG (Retrieval-Augmented Generation) pipelines using enterprise knowledge bases.
- check_circle Implement prompt engineering, guardrails, hallucination reduction strategies, and safety frameworks.
- check_circle Work with transformer-based architectures (GPT, LLaMA, Mistral, Falcon, etc.) and develop optimized model variants for low-latency and cost-efficient inference.
- check_circle Develop scalable ML systems including feature pipelines, training jobs, and batch/real-time inference services.
- check_circle Build and automate training, validation, and monitoring workflows for predictive and GenAI models.
- check_circle Perform offline evaluation, A/B testing, performance benchmarking, and business KPI tracking.
- check_circle Build and maintain end-to-end MLOps pipelines using:
- check_circle AWS SageMaker, Databricks, MLflow, Kubernetes, Docker, Terraform, Airflow
- check_circle Manage CICD pipelines for model deployment, versioning, reproducibility, and governance.
- check_circle Implement enterprise-grade model monitoring (data drift, performance, cost, safety).
- check_circle Maintain infrastructure for vector stores, embeddings pipelines, feature stores, and inference endpoints.
- check_circle Build data pipelines for structured and unstructured data using:
- check_circle Snowflake, S3, Kafka, Delta Lake, Spark (PySpark)
- check_circle Work on data ingestion, transformation, quality checks, cataloging, and secure storage.
- check_circle Ensure all systems adhere to Apexon and client-specific security, IAM, and compliance standards.
- check_circle Partner with product managers, data engineers, cloud architects, and QA teams.
- check_circle Translate business requirements into scalable AI/ML solutions.
- check_circle Ensure model explainability, governance documentation, and compliance adherence.
Basic qualifications
- Bachelor’s or Master’s 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.
Tags & Focus Areas
Fulltime Ai Ai Engineer Machine Learning Mlops Generative Ai
About Apexon
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.