Timezone - London time (1:30 PM to 10:30 PM )
Budget - 1 - 1.10 LPM (Slightly Negotiable)
Work Mode - Remote
Position overview
We are seeking a skilled AI Engineer to design, develop, and deploy AI-ML driven solutions that enhance business capabilities, automate processes, and improve customer and employee experiences. The ideal candidate has a strong foundation in machine learning, large language models (LLMs), data engineering, and cloud platforms, with the ability to productionize models at scale.
Responsibilities
- Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines.
- Develop scalable AI services and microservices using Python, REST APIs, and cloud native technologies.
Optimize models for performance, accuracy, and cost efficiency.
- Work with structured and unstructured datasets for feature engineering, vectorization, and model training.
Build data pipelines for training, validation, and inference.
- Collaborate with data engineering teams on data ingestion, storage, and governance.
- Implement CI/CD pipelines for machine learning models and MLOps workflows.
- Monitor model performance and drift, and implement retraining strategies.
- Manage model lifecycle processes, logging, and observability.
- Integrate AI systems with enterprise applications, APIs, and cloud platforms such as Azure, AWS, and GCP.
- Build Retrieval Augmented Generation (RAG) architectures leveraging vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search.
- Ensure solutions align with enterprise security, compliance, and responsible AI standards.
- Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions.
- Communicate AI capabilities and limitations to non-technical stakeholders.
- Conduct proofs of concept (POCs), demonstrations, and conceptual solution design activities.
Requirements
- Strong proficiency in Python, including NumPy, Pandas, PyTorch, TensorFlow, and Transformers minimum 5-6 years.
- Hands-on experience with LLMs, including OpenAI, Azure OpenAI, Anthropic, and Llama models.
- Experience with machine learning algorithms, natural language processing (NLP), deep learning, and vector embeddings.
- Experience with cloud platforms such as Azure, AWS, and GCP, including serverless computing services.
- Familiarity with MLOps tools such as MLflow, Kubeflow, Azure Machine Learning, Amazon SageMaker, or Databricks.
- Experience working with vector databases, including Pinecone, Chroma, FAISS, and Azure AI Search.
- Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
While sharing the profile of your resource, share the below details of candidates along with their original LinkedIn IDs
Technology / Skill Area
Candidate Experience (Years)
Self Rating (Out of 10)
1
Overall Years of Experience
2
Relevant Experience
3
Overall Salesforce Experience
/10
4
Python (NumPy, Pandas, OOP, APIs)
/10
5
Machine Learning & Deep Learning
/10
6
NLP & Transformer Models
/10
7
Large Language Models (OpenAI, Azure OpenAI, Claude, Llama)
/10
8
RAG (Retrieval Augmented Generation)
/10
9
Vector Databases (Pinecone, FAISS, Chroma, Weaviate, Azure AI Search)
/10
10
PyTorch / TensorFlow
/10
11
MLOps (MLflow, Kubeflow, SageMaker, Azure ML, Databricks)
/10
12
Cloud Platforms (Azure / AWS / GCP)
/10
13
Docker & Kubernetes
/10
14
REST APIs & AI Microservices
/10
15
CI/CD, Model Deployment & Monitoring
/10
16
Production AI Projects / Enterprise AI Solutions
/10
17
Educational Qualification (Mention the course name with Passout Year )
18
Certifications (List all the certifications and share the soft copy or screenshots of certificates - if any )
19
Currently engaged in any project (If yes, when was the last project completed)
Sign in
To continue your applicationor continue with email
By continuing you agree to our Terms & Privacy Policy.
