What are the responsibilities and job description for the AI/ML Engineer position at Adroit Innovative Solutions?
Job Description
Job Description
Job : AI / ML Engineer (Generative AI)
Location : Dallas, TX (Local Candidates Only)| Hybrid / Onsite |
Experience : 8 Yrs
Please Find the Below Requirement If You're Interested, Please Share Me your Updated Resume To rakeshmanda@adroitinnovative.com
Job Description :
We are seeking a highly skilled AI / ML Engineer with expertise in Generative AI, Python, NumPy, Pandas, AWS, and GCP to join our innovative team in Dallas, TX. The ideal candidate will have 7-8 years of experience in designing, developing, and deploying AI / ML models and solutions, particularly leveraging LLMs (Large Language Models), deep learning frameworks, and cloud services.
Key Responsibilities :
Develop, train, and optimize Generative AI models (GPT, BERT, LLaMA, T5, etc.) for various business applications.
Design and implement machine learning pipelines using Python, NumPy, Pandas, and cloud-based services (AWS / GCP).
Work with LLMs, transformers, diffusion models, and NLP techniques to improve AI-driven solutions.
Develop scalable AI / ML solutions on AWS (SageMaker, Lambda, Bedrock, S3, DynamoDB, EC2) and GCP (Vertex AI, BigQuery, Cloud Functions, AI Platform).
Implement data preprocessing, feature engineering, and model tuning to enhance model performance.
Deploy AI / ML models using MLOps best practices, ensuring model monitoring, retraining, and performance optimization.
Collaborate with cross-functional teams to integrate AI solutions into existing applications and business processes.
Stay updated on cutting-edge AI research and advancements in Generative AI, deep learning, and cloud AI services.
Required Skills & Experience :
7-8 years of experience in AI / ML engineering, with a strong focus on Generative AI and Deep Learning.
Proficiency in Python, with hands-on experience using NumPy, Pandas, TensorFlow, PyTorch, or JAX.
Expertise in LLMs, transformers, and fine-tuning pre-trained models for real-world applications.
Strong experience with AWS (SageMaker, Bedrock, Lambda, DynamoDB, S3) and GCP (Vertex AI, AI Platform, BigQuery).
Hands-on experience in building, deploying, and monitoring AI / ML models at scale.
Knowledge of MLOps tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI / CD pipelines.
Experience with data engineering pipelines, ETL workflows, and distributed computing (Spark, Dask, Ray, etc.).
Strong understanding of Neural Networks, Transformers, CNNs, RNNs, GANs, and NLP techniques.
Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.