The Role
Design, build, and scale generative AI and ML solutions including LLM fine-tuning, RAG pipelines, model training and deployment on AWS SageMaker/Bedrock, experiment tracking, and computer vision tasks. Work with vector databases, ML libraries and deep learning frameworks, and develop REST APIs and production integrations.
Summary Generated by Built In
Job Summary:
We are seeking Senior AI/ML Engineers with 3 to 6 years of experience in implementing, deploying, and scaling AI/ML solutions. This role involves working with generative AI, machine learning, deep learning, and data science to solve business challenges by designing, building, and maintaining scalable and efficient AI/ML applications.
Key Responsibilities:
AI:
- Architect scalable Generative AI and Machine Learning applications using AWS Cloud and other cutting-edge technologies.
- Extensive experience with LLMs and various prompt engineering techniques.
- Fine-tune and build custom LLMs.
- Deep understanding of LLM architecture and internal mechanisms.
- Experience with Langchain, Langgraph, Langfuse, Crew AI, LLM output evaluations, and agentic workflows.
- Build RAG (Retrieval-Augmented Generation) pipelines and integrate them with traditional applications.
Data Science & Machine Learning:
- Solve complex data science problems and uncover insights using advanced EDA techniques.
- Implement automated pipelines for data cleaning, preprocessing, and model re-training.
- Hands-on experience with model experiment tracking and validation techniques.
- Deploy, track, and monitor models using AWS SageMaker.
- Strong knowledge of fundamental machine learning concepts, including supervised and unsupervised learning, deep learning, CNNs, and RNNs.
- Proficiency in working with databases for efficient data storage and retrieval.
- Experience with data warehouses and data lakes.
Computer Vision:
- Work on complex computer vision problems, including image classification, object detection, segmentation, and image captioning.
Skills & Qualifications:
- 2-3 years of experience in implementing, deploying, and scaling Generative AI solutions.
- 3-7 years of experience in NLP, Data Science, Machine Learning, and Computer Vision.
- Proficiency in Python and ML frameworks such as Langchain, Langfuse, LLAMA Index, Langgraph, Crew AI, and LLM output evaluations.
- Experience with AWS Bedrock, OpenAI GPT models (GPT-4, GPT-4o, GPT-4o-mini), and LLMs such as Claude, LLaMa, Gemini, and DeepSeek.
- Experience with vector databases like Pinecone, OpenSearch, FAISS, and Chroma, with a strong understanding of indexing mechanisms.
- Expertise in forecasting, time series analysis, and predictive analytics.
- Experience with classification, regression, clustering, and other ML models.
- Proficiency in SageMaker for model training, evaluation, and deployment.
- Hands-on experience with ML libraries such as Scikit-learn, XGBoost, LightGBM, and CatBoost.
- Experience with deep learning frameworks such as PyTorch and TensorFlow.
- Familiarity with Docker, Uvicorn, FastAPI, and Flask for REST APIs.
- Proficiency in SQL and NoSQL databases, including PostgreSQL and AWS DynamoDB.
- Experience with caching technologies such as Redis and Memcached.
Skills Required
- 3-6 years experience implementing, deploying, and scaling AI/ML solutions (senior-level)
- 2-3 years experience implementing, deploying, and scaling Generative AI solutions
- 3-7 years experience in NLP, Data Science, Machine Learning, and Computer Vision
- Proficiency in Python
- Experience with LLM frameworks and tools (Langchain, Langgraph, Langfuse, LLAMA Index, Crew AI)
- Experience fine-tuning and building custom LLMs and strong understanding of LLM architectures
- Experience with OpenAI GPT models (GPT-4, GPT-4o, GPT-4o-mini) and LLMs such as Claude, LLaMa, Gemini, DeepSeek
- Build RAG (Retrieval-Augmented Generation) pipelines and integrate with applications
- Experience with vector databases (Pinecone, OpenSearch, FAISS, Chroma) and indexing mechanisms
- Deploy, track, and monitor models using AWS SageMaker
- Experience with AWS Cloud and AWS Bedrock
- Hands-on experience with ML libraries (Scikit-learn, XGBoost, LightGBM, CatBoost)
- Experience with deep learning frameworks (PyTorch, TensorFlow)
- Proficiency with SQL and NoSQL databases, including PostgreSQL and AWS DynamoDB
- Familiarity with Docker and serving frameworks (Uvicorn, FastAPI, Flask) for REST APIs
- Experience with caching technologies such as Redis and Memcached
- Expertise in forecasting, time series analysis, and predictive analytics
- Experience solving complex computer vision problems (classification, detection, segmentation, captioning)
- Experience with automated pipelines for data cleaning, preprocessing, and model re-training
- Hands-on experience with model experiment tracking and validation techniques
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The Company
What We Do
Armakuni is a cloud-native technology consultancy and AWS Premier Partner dedicated to accelerating digital transformation for organizations worldwide. They specialize in driving digital transformation and accelerating software delivery by helping organizations use cloud-native technologies to drive growth, increase efficiency, and enhance customer value. Their expertise spans cloud advisory, deployment, integration, data engineering, and migration, positioning them as a key engineering partner for the AI era.
