Pay Growth
?
out of 5
Not enough data
Not enough info to score pay or growth
AI/ML Engineer
Job
Tata Consultancy Services Limited
Charlotte, NC (In Person)
$87,500 Salary, Full-Time
Review key factors to help you decide if the role fits your goals.
Job Security
?
out of 5
Not enough data
Calculating job security score...
Total Score
100
out of 100
Average of individual scores
Skill Insights
Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.
Job Description
Must Have Technical/Functional Skills Primary:
AI/ML engineering, LLM experience,MLflow, Azure ML Secondary:
SageMaker, Kubeflow, NLP, Agile toolsExperience:
5+ Roles & Responsibilities Position Summary The AI Agent & Data/ML Engineer will design, develop, and operationalize AI agents, large-scale data pipelines, and machine learning solutions to support enterprise automation and analytics initiatives. This role requires strong engineering skills, expertise in LLM and agentic architectures, and the ability to deploy AI systems securely and reliably within a regulated environment. Required Qualifications- 5+ years of experience in Data Engineering, ML Engineering, AI Engineering, or Software Engineering.
- Strong proficiency in Python, SQL, and one additional programming language (Scala/Java/Go).
- Hands-on experience designing AI agents, toolchains, and agent frameworks (LangChain, Semantic Kernel, LlamaIndex, AutoGen, etc.).
- Strong understanding of LLM integration, function-calling patterns, prompt engineering, and agent orchestration.
- Experience deploying ML models using MLflow, Azure ML, SageMaker, Kubeflow, or similar.
- Proficiency in TensorFlow, PyTorch, Scikit-Learn, or equivalent ML frameworks.
- Experience building scalable data pipelines using Spark, Databricks, or similar technologies.
- Strong knowledge of RAG patterns, vector databases (FAISS, Pinecone, Chroma, Milvus, Redis), and embedding optimization.
- Experience with CI/CD pipelines, automated testing, code reviews, and deployment automation.
- Familiarity with containerization (Docker) and orchestration (Kubernetes). Desired Qualifications
- Experience integrating AI agents with enterprise APIs, microservices, and workflow systems.
- Familiarity with reasoning models, transformer architectures, and enterprise AI use cases.
- Knowledge of cloud platforms: Azure (preferred), AWS, or GCP.
- Experience with streaming technologies (Kafka, EventHub, Kinesis).
- Understanding of Responsible AI concepts, model fairness, bias detection, and safety filters.
- Experience with high-compliance environments (financial services, healthcare, or government).
- Industry certifications are a plus (e.